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Juyal A, Bisht S, Singh MF. Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring. Blood Press Monit 2024:00126097-990000000-00112. [PMID: 38958493 DOI: 10.1097/mbp.0000000000000711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodic blood pressure monitoring and prescribing antihypertensive drugs. Smart technology integration in healthcare offers promising results in optimizing the diagnosis and treatment of various conditions. We investigate its role in improving hypertension diagnosis and treatment effectiveness using machine learning algorithms for early and accurate detection. Intelligent models trained on diverse datasets (encompassing physiological parameters, lifestyle factors, and genetic information) to detect subtle hypertension risk patterns. Adaptive algorithms analyze patient-specific data, optimizing treatment plans based on medication responses and lifestyle habits. This personalized approach ensures effective, minimally invasive interventions tailored to each patient. Wearables and smart sensors provide real-time health insights for proactive treatment adjustments and early complication detection.
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Affiliation(s)
- Anubhuti Juyal
- Department of Pharmacology, Amity Institute of Pharmacy, Amity University, Lucknow, Uttar Pradesh
| | - Shradha Bisht
- Department of Pharmacology, Amity Institute of Pharmacy, Amity University, Lucknow, Uttar Pradesh
| | - Mamta F Singh
- Department of Pharmacology, College of Pharmacy, COER University, Roorkee, Uttarakhand, India
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2
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El Sherbini A, Rosenson RS, Al Rifai M, Virk HUH, Wang Z, Virani S, Glicksberg BS, Lavie CJ, Krittanawong C. Artificial intelligence in preventive cardiology. Prog Cardiovasc Dis 2024; 84:76-89. [PMID: 38460897 DOI: 10.1016/j.pcad.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 03/03/2024] [Indexed: 03/11/2024]
Abstract
Artificial intelligence (AI) is a field of study that strives to replicate aspects of human intelligence into machines. Preventive cardiology, a subspeciality of cardiovascular (CV) medicine, aims to target and mitigate known risk factors for CV disease (CVD). AI's integration into preventive cardiology may introduce novel treatment interventions and AI-centered clinician assistive tools to reduce the risk of CVD. AI's role in nutrition, weight loss, physical activity, sleep hygiene, blood pressure, dyslipidemia, smoking, alcohol, recreational drugs, and mental health has been investigated. AI has immense potential to be used for the screening, detection, and monitoring of the mentioned risk factors. However, the current literature must be supplemented with future clinical trials to evaluate the capabilities of AI interventions for preventive cardiology. This review discusses present examples, potentials, and limitations of AI's role for the primary and secondary prevention of CVD.
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Affiliation(s)
- Adham El Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
| | - Robert S Rosenson
- Cardiometabolics Unit, Mount Sinai Hospital, Mount Sinai Heart, NY, United States of America
| | - Mahmoud Al Rifai
- Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America
| | - Hafeez Ul Hassan Virk
- Harrington Heart & Vascular Institute, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America
| | - Zhen Wang
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America
| | - Salim Virani
- Section of Cardiology, The Aga Khan University, Texas Heart Institute, Baylor College of Medicine, Houston, TX, United States of America
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Carl J Lavie
- John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine, New Orleans, LA, USA
| | - Chayakrit Krittanawong
- Cardiology Division, NYU Langone Health and NYU School of Medicine, New York, NY, United States of America.
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Al-Zubayer MA, Alam K, Shanto HH, Maniruzzaman M, Majumder UK, Ahammed B. Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh. J Biosoc Sci 2024; 56:426-444. [PMID: 38505939 DOI: 10.1017/s0021932024000063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Increasing prevalence of non-communicable diseases (NCDs) has become the leading cause of death and disability in Bangladesh. Therefore, this study aimed to measure the prevalence of and risk factors for double and triple burden of NCDs (DBNCDs and TBNCDs), considering diabetes, hypertension, and overweight and obesity as well as establish a machine learning approach for predicting DBNCDs and TBNCDs. A total of 12,151 respondents from the 2017 to 2018 Bangladesh Demographic and Health Survey were included in this analysis, where 10%, 27.4%, and 24.3% of respondents had diabetes, hypertension, and overweight and obesity, respectively. Chi-square test and multilevel logistic regression (LR) analysis were applied to select factors associated with DBNCDs and TBNCDs. Furthermore, six classifiers including decision tree (DT), LR, naïve Bayes (NB), k-nearest neighbour (KNN), random forest (RF), and extreme gradient boosting (XGBoost) with three cross-validation protocols (K2, K5, and K10) were adopted to predict the status of DBNCDs and TBNCDs. The classification accuracy (ACC) and area under the curve (AUC) were computed for each protocol and repeated 10 times to make them more robust, and then the average ACC and AUC were computed. The prevalence of DBNCDs and TBNCDs was 14.3% and 2.3%, respectively. The findings of this study revealed that DBNCDs and TBNCDs were significantly influenced by age, sex, marital status, wealth index, education and geographic region. Compared to other classifiers, the RF-based classifier provides the highest ACC and AUC for both DBNCDs (ACC = 81.06% and AUC = 0.93) and TBNCDs (ACC = 88.61% and AUC = 0.97) for the K10 protocol. A combination of considered two-step factor selections and RF-based classifier can better predict the burden of NCDs. The findings of this study suggested that decision-makers might adopt suitable decisions to control and prevent the burden of NCDs using RF classifiers.
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Affiliation(s)
| | - Khorshed Alam
- School of Business, University of Southern Queensland, Toowoomba, QLD, Australia
- Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, Australia
| | | | - Md Maniruzzaman
- Statistics Discipline, Khulna University, Khulna, Bangladesh
| | | | - Benojir Ahammed
- Statistics Discipline, Khulna University, Khulna, Bangladesh
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4
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Layton AT. AI, Machine Learning, and ChatGPT in Hypertension. Hypertension 2024; 81:709-716. [PMID: 38380541 DOI: 10.1161/hypertensionaha.124.19468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Hypertension, a leading cause of cardiovascular disease and premature death, remains incompletely understood despite extensive research. Indeed, even though numerous drugs are available, achieving adequate blood pressure control remains a challenge, prompting recent interest in artificial intelligence. To promote the use of machine learning in cardiovascular medicine, this review provides a brief introduction to machine learning and reviews its notable applications in hypertension management and research, such as disease diagnosis and prognosis, treatment decisions, and omics data analysis. The challenges and limitations associated with data-driven predictive techniques are also discussed. The goal of this review is to raise awareness and encourage the hypertension research community to consider machine learning as a key component in developing innovative diagnostic and therapeutic tools for hypertension. By integrating traditional cardiovascular risk factors with genomics, socioeconomic, behavioral, and environmental factors, machine learning may aid in the development of precise risk prediction models and personalized treatment approaches for patients with hypertension.
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Affiliation(s)
- Anita T Layton
- Department of Applied Mathematics, Department of Biology, Cheriton School of Computer Science, and School of Pharmacology, University of Waterloo, Ontario, Canada
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Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, Prabhu MA, Hegde A, Salvi M, Yeong CH, Barua PD, Molinari F, Acharya UR. Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023). Comput Biol Med 2024; 172:108207. [PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, 576104, India
| | - M Maithri
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mukund A Prabhu
- Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Manipal Hospitals, Bengaluru, Karnataka, 560102, India
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
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Lin W, Shi S, Lan H, Wang N, Huang H, Wen J, Chen G. Identification of influence factors in overweight population through an interpretable risk model based on machine learning: a large retrospective cohort. Endocrine 2024; 83:604-614. [PMID: 37776483 DOI: 10.1007/s12020-023-03536-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 09/12/2023] [Indexed: 10/02/2023]
Abstract
BACKGROUND The identification of associated overweight risk factors is crucial to future health risk predictions and behavioral interventions. Several consensus problems remain in machine learning, such as cross-validation, and the resulting model may suffer from overfitting or poor interpretability. METHODS This study employed nine commonly used machine learning methods to construct overweight risk models. The general community are the target of this study, and a total of 10,905 Chinese subjects from Ningde City in Fujian province, southeast China, participated. The best model was selected through appropriate verification and validation and was suitably explained. RESULTS The overweight risk models employing machine learning exhibited good performance. It was concluded that CatBoost, which is used in the construction of clinical risk models, may surpass previous machine learning methods. The visual display of the Shapley additive explanation value for the machine model variables accurately represented the influence of each variable in the model. CONCLUSIONS The construction of an overweight risk model using machine learning may currently be the best approach. Moreover, CatBoost may be the best machine learning method. Furthermore, combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.
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Affiliation(s)
- Wei Lin
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China.
| | - Songchang Shi
- Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital Jinshan Branch, Fujian Provincial Hospital, Fuzhou, 350001, PR China
| | - Huiyu Lan
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China
| | - Nengying Wang
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China
| | - Huibin Huang
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China
| | - Junping Wen
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China
| | - Gang Chen
- Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, FuZhou, 350001, PR China.
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Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024; 16:e55869. [PMID: 38595869 PMCID: PMC11002715 DOI: 10.7759/cureus.55869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2024] [Indexed: 04/11/2024] Open
Abstract
Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.
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Affiliation(s)
| | - Mishael Awe
- Internal Medicine, Crimea State Medical University named after S.I Georgievsky, Simferopol, UKR
| | - Selvambigay Rajavelu
- Internal Medicine, Sri Ramachandra Institute of Higher Education and Research, Chennai, IND
| | - Kudapa Jahnavi
- Internal Medicine, Pondicherry Institute of Medical Sciences, Puducherry, IND
| | - Rohan Shastry
- Internal Medicine, Vydehi Institute of Medical Sciences and Research Center, Bengaluru, IND
| | - Ali Hasan
- Internal Medicine, University of Illinois at Chicago, Chicago, USA
| | - Hadi Hasan
- Internal Medicine, University of Illinois, Chicago, USA
| | | | | | - Brian Criollo Gutiérrez
- Health Sciences, Instituto Colombiano de Estudios Superiores de Incolda (ICESI) University, Cali, COL
| | - Ali Haider
- Allied Health Sciences, The University of Lahore, Gujrat, PAK
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Nosrati M, Seifi N, Hosseini N, Ferns GA, Kimiafar K, Ghayour-Mobarhan M. Essential dataset features in a successful obesity registry: a systematic review. Int Health 2024:ihae017. [PMID: 38366720 DOI: 10.1093/inthealth/ihae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 01/17/2024] [Accepted: 01/30/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND The prevalence of obesity and the diversity of available treatments makes the development of a national obesity registry desirable. To do this, it is essential to design a minimal dataset to meet the needs of a registry. This review aims to identify the essential elements of a successful obesity registry. METHODS We conducted a systematic literature review adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis recommendations. Google Scholar, Scopus and PubMed databases and Google sites were searched to identify articles containing obesity or overweight registries or datasets of obesity. We included English articles up to January 2023. RESULTS A total of 82 articles were identified. Data collection of all registries was carried out via a web-based system. According to the included datasets, the important features were as follows: demographics, anthropometrics, medical history, lifestyle assessment, nutritional assessment, weight history, clinical information, medication history, family medical history, prenatal history, quality-of-life assessment and eating disorders. CONCLUSIONS In this study, the essential features in the obesity registry dataset were demographics, anthropometrics, medical history, lifestyle assessment, nutritional assessment, weight history and clinical analysis items.
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Affiliation(s)
- Mina Nosrati
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Najmeh Seifi
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nafiseh Hosseini
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Brighton, UK
| | - Khalil Kimiafar
- Department of Medical Records and Health Information Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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Zhao Z, Lu H, Meng R, Si Z, Wang H, Wang X, Chen J, Zheng Y, Wang H, Hu J, Zhao Z, Zhu H, Wu J, Li X, Xue L. Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset. Lipids Health Dis 2024; 23:10. [PMID: 38191357 PMCID: PMC10773057 DOI: 10.1186/s12944-023-01994-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/27/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health. METHODS This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models-XG Boost, Support Vector Machine (SVM), and Random Forest (RF)-were employed. RESULTS Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance. CONCLUSIONS The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.
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Affiliation(s)
- Zekun Zhao
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Haipeng Lu
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Rui Meng
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Zhikang Si
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Hui Wang
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Xuelin Wang
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Jiaqi Chen
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Yizhan Zheng
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Huan Wang
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Jiaqi Hu
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Ziqi Zhao
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Hongmin Zhu
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Jianhui Wu
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China
| | - Xiaoming Li
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China.
| | - Ling Xue
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, 063210, China.
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Venkatachala Appa Swamy M, Periyasamy J, Thangavel M, Khan SB, Almusharraf A, Santhanam P, Ramaraj V, Elsisi M. Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction. Diagnostics (Basel) 2023; 13:diagnostics13111942. [PMID: 37296794 DOI: 10.3390/diagnostics13111942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023] Open
Abstract
With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.
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Affiliation(s)
| | - Jayalakshmi Periyasamy
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Muthamilselvan Thangavel
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Surbhi B Khan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
- Department of Data Science, School of Science, Engineering and Environment, University of Sanford, Manchester M5 4WT, UK
| | - Ahlam Almusharraf
- Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Prasanna Santhanam
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Vijayan Ramaraj
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Mahmoud Elsisi
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
- Department of Electrical Engineering, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., Cairo P.O. Box 11241, Egypt
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Sharif MS, Raj Theeng Tamang M, Fu CHY, Baker A, Alzahrani AI, Alalwan N. An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:3274. [PMID: 36991984 PMCID: PMC10055922 DOI: 10.3390/s23063274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/12/2023] [Accepted: 03/15/2023] [Indexed: 06/19/2023]
Abstract
Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.
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Affiliation(s)
- Mhd Saeed Sharif
- Intelligent Technologies Research Group, ACE, UEL, University Way, London E16 2RD, UK
| | | | - Cynthia H Y Fu
- School of Psychology, UEL, Water Lane, London E15 4LZ, UK
| | - Aaron Baker
- School of Psychology, UEL, Water Lane, London E15 4LZ, UK
| | - Ahmed Ibrahim Alzahrani
- Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
| | - Nasser Alalwan
- Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
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12
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Visco V, Izzo C, Mancusi C, Rispoli A, Tedeschi M, Virtuoso N, Giano A, Gioia R, Melfi A, Serio B, Rusciano MR, Di Pietro P, Bramanti A, Galasso G, D’Angelo G, Carrizzo A, Vecchione C, Ciccarelli M. Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. J Cardiovasc Dev Dis 2023; 10:jcdd10020074. [PMID: 36826570 PMCID: PMC9963880 DOI: 10.3390/jcdd10020074] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.
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Affiliation(s)
- Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Carmine Izzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University of Naples, 80138 Naples, Italy
| | - Antonella Rispoli
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Michele Tedeschi
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Nicola Virtuoso
- Cardiology Unit, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, 84131 Salerno, Italy
| | - Angelo Giano
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Renato Gioia
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Americo Melfi
- Cardiology Unit, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, 84131 Salerno, Italy
| | - Bianca Serio
- Hematology and Transplant Center, University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, 84131 Salerno, Italy
| | - Maria Rosaria Rusciano
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Paola Di Pietro
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Alessia Bramanti
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Gennaro Galasso
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Gianni D’Angelo
- Department of Computer Science, University of Salerno, 84084 Fisciano, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, 86077 Pozzilli, Italy
| | - Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
- Correspondence:
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Jeon J, Lee S, Oh C. Age-specific risk factors for the prediction of obesity using a machine learning approach. Front Public Health 2023; 10:998782. [PMID: 36733276 PMCID: PMC9887184 DOI: 10.3389/fpubh.2022.998782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 12/06/2022] [Indexed: 01/18/2023] Open
Abstract
Machine Learning is a powerful tool to discover hidden information and relationships in various data-driven research fields. Obesity is an extremely complex topic, involving biological, physiological, psychological, and environmental factors. One successful approach to the topic is machine learning frameworks, which can reveal complex and essential risk factors of obesity. Over the last two decades, the obese population (BMI of above 23) in Korea has grown. The purpose of this study is to identify risk factors that predict obesity using machine learning classifiers and identify the algorithm with the best accuracy among classifiers used for obesity prediction. This work will allow people to assess obesity risk from blood tests and blood pressure data based on the KNHANES, which used data constructed by the annual survey. Our data include a total of 21,100 participants (male 10,000 and female 11,100). We assess obesity prediction by utilizing six machine learning algorithms. We explore age- and gender-specific risk factors of obesity for adults (19-79 years old). Our results highlight the four most significant features in all age-gender groups for predicting obesity: triglycerides, ALT (SGPT), glycated hemoglobin, and uric acid. Our findings show that the risk factors for obesity are sensitive to age and gender under different machine learning algorithms. Performance is highest for the 19-39 age group of both genders, with over 70% accuracy and AUC, while the 60-79 age group shows around 65% accuracy and AUC. For the 40-59 age groups, the proposed algorithm achieved over 70% in AUC, but for the female participants, it achieved lower than 70% accuracy. For all classifiers and age groups, there is no big difference in the accuracy ratio when the number of features is more than six; however, the accuracy ratio decreased in the female 19-39 age group.
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Affiliation(s)
- Junhwi Jeon
- Department of Applied Mathematics, Kyung Hee University, Yongin, South Korea
| | - Sunmi Lee
- Department of Applied Mathematics, Kyung Hee University, Yongin, South Korea
| | - Chunyoung Oh
- Department of Mathematics Education, Chonnam National University, Gwangju, South Korea,*Correspondence: Chunyoung Oh ✉
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Kanyongo W, Ezugwu AE. Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
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15
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Arora A, Arora A. Machine learning models trained on synthetic datasets of multiple sample sizes for the use of predicting blood pressure from clinical data in a national dataset. PLoS One 2023; 18:e0283094. [PMID: 36928534 PMCID: PMC10019654 DOI: 10.1371/journal.pone.0283094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023] Open
Abstract
INTRODUCTION The potential for synthetic data to act as a replacement for real data in research has attracted attention in recent months due to the prospect of increasing access to data and overcoming data privacy concerns when sharing data. The field of generative artificial intelligence and synthetic data is still early in its development, with a research gap evidencing that synthetic data can adequately be used to train algorithms that can be used on real data. This study compares the performance of a series machine learning models trained on real data and synthetic data, based on the National Diet and Nutrition Survey (NDNS). METHODS Features identified to be potentially of relevance by directed acyclic graphs were isolated from the NDNS dataset and used to construct synthetic datasets and impute missing data. Recursive feature elimination identified only four variables needed to predict mean arterial blood pressure: age, sex, weight and height. Bayesian generalised linear regression, random forest and neural network models were constructed based on these four variables to predict blood pressure. Models were trained on the real data training set (n = 2408), a synthetic data training set (n = 2408) and larger synthetic data training set (n = 4816) and a combination of the real and synthetic data training set (n = 4816). The same test set (n = 424) was used for each model. RESULTS Synthetic datasets demonstrated a high degree of fidelity with the real dataset. There was no significant difference between the performance of models trained on real, synthetic or combined datasets. Mean average error across all models and all training data ranged from 8.12 To 8.33. This indicates that synthetic data was capable of training equally accurate machine learning models as real data. DISCUSSION Further research is needed on a variety of datasets to confirm the utility of synthetic data to replace the use of potentially identifiable patient data. There is also further urgent research needed into evidencing that synthetic data can truly protect patient privacy against adversarial attempts to re-identify real individuals from the synthetic dataset.
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Affiliation(s)
- Anmol Arora
- School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| | - Ananya Arora
- School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
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16
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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17
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Wang T, Yan Y, Xiang S, Tan J, Yang C, Zhao W. A comparative study of antihypertensive drugs prediction models for the elderly based on machine learning algorithms. Front Cardiovasc Med 2022; 9:1056263. [PMID: 36531716 PMCID: PMC9753549 DOI: 10.3389/fcvm.2022.1056263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/17/2022] [Indexed: 11/04/2023] Open
Abstract
Background Globally, blood pressure management strategies were ineffective, and a low percentage of patients receiving hypertension treatment had their blood pressure controlled. In this study, we aimed to build a medication prediction model by correlating patient attributes with medications to help physicians quickly and rationally match appropriate medications. Methods We collected clinical data from elderly hypertensive patients during hospitalization and combined statistical methods and machine learning (ML) algorithms to filter out typical indicators. We constructed five ML models to evaluate all datasets using 5-fold cross-validation. Include random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models. And the performance of the models was evaluated using the micro-F1 score. Results Our experiments showed that by statistical methods and ML algorithms for feature selection, we finally selected Age, SBP, DBP, Lymph, RBC, HCT, MCHC, PLT, AST, TBIL, Cr, UA, Urea, K, Na, Ga, TP, GLU, TC, TG, γ-GT, Gender, HTN CAD, and RI as feature metrics of the models. LightGBM had the best prediction performance with the micro-F1 of 78.45%, which was higher than the other four models. Conclusion LightGBM model has good results in predicting antihypertensive medication regimens, and the model can be beneficial in improving the personalization of hypertension treatment.
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Affiliation(s)
- Tiantian Wang
- School of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Yongjie Yan
- Medical Records and Statistics Office, The Third Affiliated Hospital of Army Medical University, Chongqing, China
| | - Shoushu Xiang
- Medical Records and Statistics Room, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Juntao Tan
- Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Chen Yang
- School of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Wenlong Zhao
- School of Medical Informatics, Chongqing Medical University, Chongqing, China
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18
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Haugg F, Elgendi M, Menon C. Assessment of Blood Pressure Using Only a Smartphone and Machine Learning Techniques: A Systematic Review. Front Cardiovasc Med 2022; 9:894224. [PMID: 35770219 PMCID: PMC9234172 DOI: 10.3389/fcvm.2022.894224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/06/2022] [Indexed: 11/28/2022] Open
Abstract
Regular monitoring of blood pressure (BP) allows for early detection of hypertension and symptoms related to cardiovascular disease. Measuring BP with a cuff requires equipment that is not always readily available and it may be impractical for some patients. Smartphones are an integral part of the lives of most people; thus, detecting and monitoring hypertension with a smartphone is likely to increase the ability to monitor BP due to the convenience of use for many patients. Smartphones lend themselves to assessing cardiovascular health because their built-in sensors and cameras provide a means of detecting arterial pulsations. To this end, several image processing and machine learning (ML) techniques for predicting BP using a smartphone have been developed. Several ML models that utilize smartphones are discussed in this literature review. Of the 53 papers identified, seven publications were evaluated. The performance of the ML models was assessed based on their accuracy for classification, the mean error measure, and the standard deviation of error for regression. It was found that artificial neural networks and support vector machines were often used. Because a variety of influencing factors determines the performance of an ML model, no clear preference could be determined. The number of input features ranged from five to 233, with the most commonly used being demographic data and the features extracted from photoplethysmogram signals. Each study had a different number of participants, ranging from 17 to 5,992. Comparisons of the cuff-based measures were mostly used to validate the results. Some of these ML models are already used to detect hypertension and BP but, to satisfy possible regulatory demands, improved reliability is needed under a wider range of conditions, including controlled and uncontrolled environments. A discussion of the advantages of various ML techniques and the selected features is offered at the end of this systematic review.
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Affiliation(s)
- Fridolin Haugg
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland.,Department of Mechanical Engineering, Karlsruher Institute for Technology, Karlsruhe, Germany
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
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19
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He L, Liu H, Yang Y, Wang B. A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3471571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
We develop a deep learning model based on Long Short-term Memory (LSTM) to predict blood pressure based on a unique data set collected from physical examination centers capturing comprehensive multi-year physical examination and lab results. In the Multi-attention Collaborative Deep Learning model (MAC-LSTM) we developed for this type of data, we incorporate three types of attention to generate more explainable and accurate results. In addition, we leverage information from similar users to enhance the predictive power of the model due to the challenges with short examination history. Our model significantly reduces predictive errors compared to several state-of-the-art baseline models. Experimental results not only demonstrate our model’s superiority but also provide us with new insights about factors influencing blood pressure. Our data is collected in a natural setting instead of a setting designed specifically to study blood pressure, and the physical examination items used to predict blood pressure are common items included in regular physical examinations for all the users. Therefore, our blood pressure prediction results can be easily used in an alert system for patients and doctors to plan prevention or intervention. The same approach can be used to predict other health-related indexes such as BMI.
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Affiliation(s)
- Luo He
- Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China
| | - Hongyan Liu
- Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China
| | - Yinghui Yang
- Graduate School of Management, University of California, California, USA
| | - Bei Wang
- School of Information, Renmin University of China, Beijing, China
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20
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Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:5616939. [PMID: 35685669 PMCID: PMC9168207 DOI: 10.1155/2022/5616939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/28/2022] [Accepted: 05/09/2022] [Indexed: 11/18/2022]
Abstract
Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.6% of men and 20.9% of women. It was estimated that this trend will increase to 22.9% and 23.6% for men and women, respectively, by 2025. Controlling blood pressure is therefore important to lower both morbidity and mortality. Computer-aided diagnosis (CAD) is a noninvasive technique which can determine subtle myocardial structural changes at an early stage. In this work, we show how a multi-resolution analysis-based CAD system can be utilized for the detection of early HTN-induced left ventricular heart muscle changes with the help of ultrasound imaging. Firstly, features were extracted from the ultrasound imagery, and then the feature dimensions were reduced using a locality sensitive discriminant analysis (LSDA). The decision tree classifier with contourlet and shearlet transform features was later employed for improved performance and maximized accuracy using only two features. The developed model is applicable for the evaluation of cardiac structural alteration in HTN and can be used as a standalone tool in hospitals and polyclinics.
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21
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Austin PC, Harrell FE, Lee DS, Steyerberg EW. Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure. Sci Rep 2022; 12:9312. [PMID: 35660759 PMCID: PMC9166797 DOI: 10.1038/s41598-022-13015-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/19/2022] [Indexed: 12/20/2022] Open
Abstract
Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods perform better than statistical learning methods in a specific setting. We evaluated six learning methods: stochastic gradient boosting machines using trees as the base learners, random forests, artificial neural networks, the lasso, ridge regression, and linear regression estimated using ordinary least squares (OLS). Our simulations were informed by empirical analyses in patients with acute myocardial infarction (AMI) and congestive heart failure (CHF) and used six data-generating processes, each based on one of the six learning methods, to simulate continuous outcomes in the derivation and validation samples. The outcome was systolic blood pressure at hospital discharge, a continuous outcome. We applied the six learning methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples. The primary observation was that neural networks tended to result in estimates with worse predictive accuracy than the other five methods in both disease samples and across all six data-generating processes. Boosted trees and OLS regression tended to perform well across a range of scenarios.
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Affiliation(s)
- Peter C Austin
- ICES, G106, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada. .,Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. .,Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, ON, Canada.
| | - Frank E Harrell
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Douglas S Lee
- ICES, G106, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.,Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.,Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
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22
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EL-Rahman SA, Saleh Alluhaidan A, AlRashed RA, AlZunaytan DN. Chronic diseases monitoring and diagnosis system based on features selection and machine learning predictive models. Soft comput 2022. [DOI: 10.1007/s00500-022-07130-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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23
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Xiang Y, Li S, Zhang P. An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management. FUNDAMENTAL RESEARCH 2022; 2:154-165. [PMID: 38933904 PMCID: PMC11197610 DOI: 10.1016/j.fmre.2021.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 09/02/2021] [Accepted: 11/12/2021] [Indexed: 11/24/2022] Open
Abstract
Blood pressure (BP) is an important indicator of an individual's health status and is closely related to daily behaviors. Thus, a continuous daily measurement of BP is critical for hypertension control. To assist continuous measurement, BP prediction based on non-physiological data (ubiquitous mobile phone data) was studied in the research. An algorithm was proposed that predicts BP based on patients' daily routine, which includes activities such as sleep, work, and commuting. The aim of the research is to provide insight into the application of mobile data in telemonitoring and the continuous unobtrusive daily measurement of BP. A half-year data set from October 2017 of 320 individuals, including telecom data and BP measurement data, was analyzed. Two hierarchical Bayesian topic models were used to extract individuals' location-driven daily routine patterns (topics) and calculate probabilities among these topics from their day-level mobile trajectories. Based on the topic probability distribution and patients' contextual data, their BP were predicted using different models. The prediction model comparison shows that the long short-term memory (LSTM) method exceeds others when the data has a high dependency. Otherwise, the Random Forest regression model outperforms the LSTM method. Also, the experimental results validate the effectiveness of the topics in BP prediction.
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Affiliation(s)
- Yidan Xiang
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shaochun Li
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Pengzhu Zhang
- Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
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24
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Deriving a mapping function to tie anthropometric measurements to body mass index via interpretable machine learning. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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25
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Islam MM, Shamsuddin R. Machine learning to promote health management through lifestyle changes for hypertension patients. ARRAY 2021. [DOI: 10.1016/j.array.2021.100090] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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26
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Martinez-Ríos E, Montesinos L, Alfaro-Ponce M, Pecchia L. A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102813] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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27
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Thamrin SA, Arsyad DS, Kuswanto H, Lawi A, Nasir S. Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018. Front Nutr 2021; 8:669155. [PMID: 34235168 PMCID: PMC8255629 DOI: 10.3389/fnut.2021.669155] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/27/2021] [Indexed: 12/22/2022] Open
Abstract
Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes.
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Affiliation(s)
- Sri Astuti Thamrin
- Department of Statistics, Faculty of Mathematics and Natural Science, Hasanuddin University, Makassar, Indonesia
| | - Dian Sidik Arsyad
- Department of Epidemiology, Faculty of Public Health, Hasanuddin University, Makassar, Indonesia
| | - Hedi Kuswanto
- Department of Statistics, Faculty of Mathematics and Natural Science, Hasanuddin University, Makassar, Indonesia
| | - Armin Lawi
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia
| | - Sudirman Nasir
- Department of Health Promotion, Faculty of Public Health, Hasanuddin University, Makassar, Indonesia
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Selya A, Anshutz D, Griese E, Weber TL, Hsu B, Ward C. Predicting unplanned medical visits among patients with diabetes: translation from machine learning to clinical implementation. BMC Med Inform Decis Mak 2021; 21:111. [PMID: 33789660 PMCID: PMC8011134 DOI: 10.1186/s12911-021-01474-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 03/22/2021] [Indexed: 01/04/2023] Open
Abstract
Background Diabetes is a medical and economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. Methods Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors. Results The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. This model outperformed a conventional logistic regression by 0.4 percentage points. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model’s overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. Conclusion Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.
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Affiliation(s)
- Arielle Selya
- Department of Population Health, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA. .,Behavioral Sciences Group, Sanford Research, 2301 East 60th Street North, Sioux Falls, SD, 57104, USA. .,Department of Pediatrics, University of South Dakota Sanford School of Medicine, Sioux Falls, SD, USA. .,Pinney Associates, Inc., Pittsburgh, PA, USA.
| | - Drake Anshutz
- Behavioral Sciences Group, Sanford Research, 2301 East 60th Street North, Sioux Falls, SD, 57104, USA.,Advanced Analytics, St. Luke's Health System, Boise, ID, USA
| | - Emily Griese
- Behavioral Sciences Group, Sanford Research, 2301 East 60th Street North, Sioux Falls, SD, 57104, USA.,Sanford Heath Plan, Sioux Falls, SD, USA
| | - Tess L Weber
- Behavioral Sciences Group, Sanford Research, 2301 East 60th Street North, Sioux Falls, SD, 57104, USA
| | - Benson Hsu
- Department of Pediatrics, University of South Dakota Sanford School of Medicine, Sioux Falls, SD, USA
| | - Cheryl Ward
- Behavioral Sciences Group, Sanford Research, 2301 East 60th Street North, Sioux Falls, SD, 57104, USA.,EDCO Health Information Systems, Sioux Falls, SD, USA
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Tsoi K, Yiu K, Lee H, Cheng HM, Wang TD, Tay JC, Teo BW, Turana Y, Soenarta AA, Sogunuru GP, Siddique S, Chia YC, Shin J, Chen CH, Wang JG, Kario K. Applications of artificial intelligence for hypertension management. J Clin Hypertens (Greenwich) 2021; 23:568-574. [PMID: 33533536 PMCID: PMC8029548 DOI: 10.1111/jch.14180] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 12/23/2020] [Accepted: 12/30/2020] [Indexed: 01/13/2023]
Abstract
The prevalence of hypertension is increasing along with an aging population, causing millions of premature deaths annually worldwide. Low awareness of blood pressure (BP) elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. The advent of artificial intelligence (AI), however, sheds the light of new strategies for hypertension management, such as remote supports from telemedicine and big data-derived prediction. There is considerable evidence demonstrating the feasibility of AI applications in hypertension management. A foreseeable trend was observed in integrating BP measurements with various wearable sensors and smartphones, so as to permit continuous and convenient monitoring. In the meantime, further investigations are advised to validate the novel prediction and prognostic tools. These revolutionary developments have made a stride toward the future model for digital management of chronic diseases.
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Affiliation(s)
- Kelvin Tsoi
- SH Big Data Decision and Analytics Research Centre, Shatin, Hong Kong
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Karen Yiu
- SH Big Data Decision and Analytics Research Centre, Shatin, Hong Kong
| | - Helen Lee
- JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Hao-Min Cheng
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan
- Institute of Public Health and Community Medicine Research Center, National Yang-Ming University School of Medicine, Taipei, Taiwan
- Center for Evidence-based Medicine, Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Tzung-Dau Wang
- Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
- Division of Hospital Medicine, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan
| | - Jam-Chin Tay
- Department of General Medicine, Tan Tock Seng Hospital, Singapore, Singapore
| | - Boon Wee Teo
- Division of Nephrology Department of Medicine, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Yuda Turana
- Department of Neurology, School of Medicine and health Sciences, Atma Jaya Catholic University of Indonesia, Indonesia
| | - Arieska Ann Soenarta
- Department of Cardiology and Vascular Medicine, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia
| | | | | | - Yook-Chin Chia
- Department of Medical Sciences, School of Healthcare and Medical Sciences, Sunway University, Bandar Sunway, Malaysia
- Faculty of Medicine, Department of Primary Care Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Jinho Shin
- Faculty of Cardiology Service, Hanyang University Medical Center, Seoul, Korea
| | - Chen-Huan Chen
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ji-Guang Wang
- Department of Hypertension, Centre for Epidemiological Studies and Clinical Trials, The Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Kazuomi Kario
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
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Guo A, Beheshti R, Khan YM, Langabeer JR, Foraker RE. Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models. BMC Med Inform Decis Mak 2021; 21:5. [PMID: 33407390 PMCID: PMC7789405 DOI: 10.1186/s12911-020-01345-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 11/22/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help providers better manage patients' CVH. We hypothesized that CVH measures can be predicted based on previous measurements from longitudinal electronic health record (EHR) data. METHODS The Guideline Advantage (TGA) dataset was used and contained EHR data from 70 outpatient clinics across the United States (US). We studied predictions of 5 CVH submetrics: smoking status (SMK), body mass index (BMI), blood pressure (BP), hemoglobin A1c (A1C), and low-density lipoprotein (LDL). We applied embedding techniques and long short-term memory (LSTM) networks - to predict future CVH category levels from all the previous CVH measurements of 216,445 unique patients for each CVH submetric. RESULTS The LSTM model performance was evaluated by the area under the receiver operator curve (AUROC): the micro-average AUROC was 0.99 for SMK prediction; 0.97 for BMI; 0.84 for BP; 0.91 for A1C; and 0.93 for LDL prediction. Model performance was not improved by using all 5 submetric measures compared with using single submetric measures. CONCLUSIONS We suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients' future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.
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Affiliation(s)
- Aixia Guo
- Institute for Informatics (I2), Washington University School of Medicine, 600 S. Taylor Avenue, Suite 102, St. Louis, MO, 63110, USA.
| | - Rahmatollah Beheshti
- Department of Computer & Information Sciences, Data Science Institute, University of Delaware, Newark, DE, USA
| | - Yosef M Khan
- Health Informatics and Analytics, Centers for Health Metrics and Evaluation, American Heart Association, Dallas, TX, USA
| | - James R Langabeer
- School of Biomedical Informatics, Health Science Center at Houston, The University of Texas, Houston, TX, USA
| | - Randi E Foraker
- Institute for Informatics (I2), Washington University School of Medicine, 600 S. Taylor Avenue, Suite 102, St. Louis, MO, 63110, USA
- Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, USA
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Chaikijurajai T, Laffin LJ, Tang WHW. Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. Am J Hypertens 2020; 33:967-974. [PMID: 32615586 DOI: 10.1093/ajh/hpaa102] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 06/26/2020] [Indexed: 12/19/2022] Open
Abstract
Prevention and treatment of hypertension (HTN) are a challenging public health problem. Recent evidence suggests that artificial intelligence (AI) has potential to be a promising tool for reducing the global burden of HTN, and furthering precision medicine related to cardiovascular (CV) diseases including HTN. Since AI can stimulate human thought processes and learning with complex algorithms and advanced computational power, AI can be applied to multimodal and big data, including genetics, epigenetics, proteomics, metabolomics, CV imaging, socioeconomic, behavioral, and environmental factors. AI demonstrates the ability to identify risk factors and phenotypes of HTN, predict the risk of incident HTN, diagnose HTN, estimate blood pressure (BP), develop novel cuffless methods for BP measurement, and comprehensively identify factors associated with treatment adherence and success. Moreover, AI has also been used to analyze data from major randomized controlled trials exploring different BP targets to uncover previously undescribed factors associated with CV outcomes. Therefore, AI-integrated HTN care has the potential to transform clinical practice by incorporating personalized prevention and treatment approaches, such as determining optimal and patient-specific BP goals, identifying the most effective antihypertensive medication regimen for an individual, and developing interventions targeting modifiable risk factors. Although the role of AI in HTN has been increasingly recognized over the past decade, it remains in its infancy, and future studies with big data analysis and N-of-1 study design are needed to further demonstrate the applicability of AI in HTN prevention and treatment.
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Affiliation(s)
- Thanat Chaikijurajai
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Luke J Laffin
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Wai Hong Wilson Tang
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio, USA
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AlJame M, Ahmad I, Imtiaz A, Mohammed A. Ensemble learning model for diagnosing COVID-19 from routine blood tests. INFORMATICS IN MEDICINE UNLOCKED 2020; 21:100449. [PMID: 33102686 PMCID: PMC7572278 DOI: 10.1016/j.imu.2020.100449] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/28/2020] [Accepted: 10/07/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The pandemic of novel coronavirus disease 2019 (COVID-19) has severely impacted human society with a massive death toll worldwide. There is an urgent need for early and reliable screening of COVID-19 patients to provide better and timely patient care and to combat the spread of the disease. In this context, recent studies have reported some key advantages of using routine blood tests for initial screening of COVID-19 patients. In this article, first we present a review of the emerging techniques for COVID-19 diagnosis using routine laboratory and/or clinical data. Then, we propose ERLX which is an ensemble learning model for COVID-19 diagnosis from routine blood tests. METHOD The proposed model uses three well-known diverse classifiers, extra trees, random forest and logistic regression, which have different architectures and learning characteristics at the first level, and then combines their predictions by using a second level extreme gradient boosting (XGBoost) classifier to achieve a better performance. For data preparation, the proposed methodology employs a KNNImputer algorithm to handle null values in the dataset, isolation forest (iForest) to remove outlier data, and a synthetic minority oversampling technique (SMOTE) to balance data distribution. For model interpretability, features importance are reported by using the SHapley Additive exPlanations (SHAP) technique. RESULTS The proposed model was trained and evaluated by using a publicly available data set from Albert Einstein Hospital in Brazil, which consisted of 5644 data samples with 559 confirmed COVID-19 cases. The ensemble model achieved outstanding performance with an overall accuracy of 99.88% [95% CI: 99.6-100], AUC of 99.38% [95% CI: 97.5-100], a sensitivity of 98.72% [95% CI: 94.6-100] and a specificity of 99.99% [95% CI: 99.99-100]. DISCUSSION The proposed model revealed better performance when compared against existing state-of-the-art studies (Banerjee et al., 2020; de Freitas Barbosa et al., 2020; de Moraes Batista et al., 2020; Soares et al., 2020) [3,22,56,71] for the same set of features employed by them. As compared to the best performing Bayes Net model (de Freitas Barbosa et al., 2020) [22] average accuracy of 95.159%, ERLX achieved an average accuracy of 99.94%. In comparison with AUC of 85% reported by the SVM model (de Moraes Batista et al., 2020) [56], ERLX obtained AUC of 99.77% in addition to improvements in sensitivity, and specificity. As compared with ER-COV model (Soares et al., 2020) [71] average sensitivity of 70.25% and specificity of 85.98%, ERLX model achieved sensitivity of 99.47% and specificity of 99.99%. The ERLX model obtained a considerably higher score as compared with ANN model (Banerjee et al., 2020) [3] in all performance metrics. Therefore, the model presented is robust and can be deployed for reliable early and rapid screening of COVID-19 patients.
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Affiliation(s)
- Maryam AlJame
- Computer Engineering Department, Kuwait University, Kuwait
| | - Imtiaz Ahmad
- Computer Engineering Department, Kuwait University, Kuwait
| | | | - Ameer Mohammed
- Computer Engineering Department, Kuwait University, Kuwait
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AlKaabi LA, Ahmed LS, Al Attiyah MF, Abdel-Rahman ME. Predicting hypertension using machine learning: Findings from Qatar Biobank Study. PLoS One 2020; 15:e0240370. [PMID: 33064740 PMCID: PMC7567367 DOI: 10.1371/journal.pone.0240370] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/08/2020] [Indexed: 12/14/2022] Open
Abstract
Background and objective Hypertension, a global burden, is associated with several risk factors and can be treated by lifestyle modifications and medications. Prediction and early diagnosis is important to prevent related health complications. The objective is to construct and compare predictive models to identify individuals at high risk of developing hypertension without the need of invasive clinical procedures. Methods This is a cross-sectional study using 987 records of Qataris and long-term residents aged 18+ years from Qatar Biobank. Percentages were used to summarize data and chi-square tests to assess associations. Predictive models of hypertension were constructed and compared using three supervised machine learning algorithms: decision tree, random forest, and logistics regression using 5-fold cross-validation. The performance of algorithms was assessed using accuracy, positive predictive value (PPV), sensitivity, F-measure, and area under the receiver operating characteristic curve (AUC). Stata and Weka were used for analysis. Results Age, gender, education level, employment, tobacco use, physical activity, adequate consumption of fruits and vegetables, abdominal obesity, history of diabetes, history of high cholesterol, and mother’s history high blood pressure were important predictors of hypertension. All algorithms showed more or less similar performances: Random forest (accuracy = 82.1%, PPV = 81.4%, sensitivity = 82.1%), logistic regression (accuracy = 81.1%, PPV = 80.1%, sensitivity = 81.1%) and decision tree (accuracy = 82.1%, PPV = 81.2%, sensitivity = 82.1%. In terms of AUC, compared to logistic regression, while random forest performed similarly, decision tree had a significantly lower discrimination ability (p-value<0.05) with AUC’s equal to 85.0, 86.9, and 79.9, respectively. Conclusions Machine learning provides the chance of having a rapid predictive model using non-invasive predictors to screen for hypertension. Future research should consider improving the predictive accuracy of models in larger general populations, including more important predictors and using a variety of algorithms.
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Affiliation(s)
- Latifa A. AlKaabi
- Department of Public Health, College of Health Science, QU Health, Qatar University, Doha, Qatar
| | - Lina S. Ahmed
- Department of Public Health, College of Health Science, QU Health, Qatar University, Doha, Qatar
| | - Maryam F. Al Attiyah
- Department of Public Health, College of Health Science, QU Health, Qatar University, Doha, Qatar
| | - Manar E. Abdel-Rahman
- Department of Public Health, College of Health Science, QU Health, Qatar University, Doha, Qatar
- * E-mail:
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Koshimizu H, Kojima R, Okuno Y. Future possibilities for artificial intelligence in the practical management of hypertension. Hypertens Res 2020; 43:1327-1337. [PMID: 32655135 DOI: 10.1038/s41440-020-0498-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 05/13/2020] [Accepted: 05/17/2020] [Indexed: 11/09/2022]
Abstract
The use of artificial intelligence in numerous prediction and classification tasks, including clinical research and healthcare management, is becoming increasingly more common. This review describes the current status and a future possibility for artificial intelligence in blood pressure management, that is, the possibility of accurately predicting and estimating blood pressure using large-scale data, such as personal health records and electronic medical records. Individual blood pressure continuously changes because of lifestyle habits and the environment. This review focuses on two topics regarding controlling changing blood pressure: a novel blood pressure measurement system and blood pressure analysis using artificial intelligence. Regarding the novel blood pressure measurement system, we compare the conventional cuff-less method with the analysis of pulse waves using artificial intelligence for blood pressure estimation. Then, we describe the prediction of future blood pressure values using machine learning and deep learning. In addition, we summarize factor analysis using "explainable AI" to solve a black-box problem of artificial intelligence. Overall, we show that artificial intelligence is advantageous for hypertension management and can be used to establish clinical evidence for the practical management of hypertension.
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Affiliation(s)
- Hiroshi Koshimizu
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.,Development Center, Omron Healthcare Co., Ltd., Kyoto, 617-0002, Japan
| | - Ryosuke Kojima
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Yasushi Okuno
- Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
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E-Health in Hypertension Management: an Insight into the Current and Future Role of Blood Pressure Telemonitoring. Curr Hypertens Rep 2020; 22:42. [PMID: 32506273 DOI: 10.1007/s11906-020-01056-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
PURPOSE OF REVIEW Out-of-office blood pressure (BP) monitoring techniques, including home and ambulatory BP monitoring, are currently recommended by hypertension guidelines worldwide to confirm the diagnosis of hypertension and to monitor the appropriateness of treatment. However, such techniques are not always effectively implemented or timely available in the routine clinical practice. In recent years, the widespread availability of e-health solutions has stimulated the development of blood pressure telemonitoring (BPT) systems, which allow remote BP tracking and tighter and more efficient monitoring of patients' health status. RECENT FINDINGS There is currently strong evidence that BPT may be of benefit for hypertension screening and diagnosis and for improving hypertension management. The advantage is more significant when BPT is coupled with multimodal interventions involving a physician, a nurse or pharmacist, and including education on lifestyle and risk factors and drug management. Several randomized controlled studies documented enhanced hypertension management and improved BP control of hypertensive patients through BPT. Potential additional effects of BPT are represented by improved compliance to treatment, intensification, and optimization of drug use, improved quality of life, reduction in risk of developing cardiovascular complications, and cost-saving. Applications based on m-health and making use of wearables or smartwatches integrated with machine learning models are particularly promising for the future development of efficient BPT solutions, and they will provide remarkable support decision tools for doctors. BPT and telehealth will soon disrupt hypertension management. However, which approach will be the most effective and whether it will be sustainable in the long-term still need to be elucidated.
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Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview. SENSORS 2020; 20:s20092734. [PMID: 32403349 PMCID: PMC7248873 DOI: 10.3390/s20092734] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/04/2020] [Accepted: 05/07/2020] [Indexed: 02/06/2023]
Abstract
Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and "obesity/overweight" is one of the consequences. "Obesity and overweight" are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The "World Health Organization" (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as "Center for Disease Control and Prevention (CDC)" and "National Institute for Clinical Excellence (NICE)" guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in "Kaggle" and "University of California, Irvine (UCI) database", and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting "obesity and overweight" as a study case in the future? (c) why have we used the existing "Kaggle" and "UCI" datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?
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Jang S, Jang YE, Kim YJ, Yu H. Input initialization for inversion of neural networks using k-nearest neighbor approach. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.01.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Prediction of blood pressure variability using deep neural networks. Int J Med Inform 2020; 136:104067. [DOI: 10.1016/j.ijmedinf.2019.104067] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 12/09/2019] [Accepted: 12/26/2019] [Indexed: 12/17/2022]
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Santhanam P, Ahima RS. Machine learning and blood pressure. J Clin Hypertens (Greenwich) 2019; 21:1735-1737. [PMID: 31536164 DOI: 10.1111/jch.13700] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 08/26/2019] [Indexed: 12/25/2022]
Abstract
Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised learning algorithms that are being used to identify and predict blood pressure (BP) and other measures of cardiovascular risk. Since 1999, starting with neural network methods, ML has been used to gauge the relationship between BP and pulse wave forms. Since then, the scope of the research has expanded to using different cardiometabolic risk factors like BMI, waist circumference, waist-to-hip ratio in concert with BP and its various pharmaceutical agents to estimate biochemical measures (like HDL cholesterol, LDL and total cholesterol, fibrinogen, and uric acid) as well as effectiveness of anti-hypertensive regimens. Data from large clinical trials like the SPRINT are being re-analyzed by ML methods to unearth new findings and identify unique relationships between predictors and outcomes. In summary, AI and ML methods are gaining immense attention in the management of chronic disease. Elevated BP is a very important early metric for the risk of development of cardiovascular and renal injury; therefore, advances in AI and ML will aid in early disease prediction and intervention.
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Affiliation(s)
- Prasanna Santhanam
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rexford S Ahima
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Chen S, Ji Z, Wu H, Xu Y. A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning. SENSORS 2019; 19:s19112585. [PMID: 31174357 PMCID: PMC6603686 DOI: 10.3390/s19112585] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 05/20/2019] [Accepted: 05/31/2019] [Indexed: 11/22/2022]
Abstract
Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this study aims to establish the systolic BP and diastolic BP estimation models based on machine learning using pulse transit time and characteristics of pulse waveform. In the process of model construction, the mean impact value method was introduced to investigate the impact of each feature on the models and the genetic algorithm was introduced to implement parameter optimization. The experimental results showed that the proposed models could effectively describe the nonlinear relationship between the features and BP and had higher accuracy than the traditional methods with the error of 3.27 ± 5.52 mmHg for systolic BP and 1.16 ± 1.97 mmHg for diastolic BP. Moreover, the estimation errors met the requirements of the Advancement of Medical Instrumentation and British Hypertension Society criteria. In conclusion, this study was helpful in promoting the practical application of methods for non-invasive continuous BP estimation models.
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Affiliation(s)
- Shuo Chen
- College of Bioengineering, Chongqing University, Chongqing 400044, China.
| | - Zhong Ji
- College of Bioengineering, Chongqing University, Chongqing 400044, China.
- Chongqing Medical Electronics Engineering Technology Center, Chongqing 400044, China.
| | - Haiyan Wu
- College of Bioengineering, Chongqing University, Chongqing 400044, China.
| | - Yingchao Xu
- College of Bioengineering, Chongqing University, Chongqing 400044, China.
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Scheinker D, Valencia A, Rodriguez F. Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models. JAMA Netw Open 2019; 2:e192884. [PMID: 31026030 PMCID: PMC6487629 DOI: 10.1001/jamanetworkopen.2019.2884] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
IMPORTANCE Obesity is a leading cause of high health care expenditures, disability, and premature mortality. Previous studies have documented geographic disparities in obesity prevalence. OBJECTIVE To identify county-level factors associated with obesity using traditional epidemiologic and machine learning methods. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study using linear regression models and machine learning models to evaluate the associations between county-level obesity and county-level demographic, socioeconomic, health care, and environmental factors from summarized statistical data extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data from each of 3138 US counties. The explanatory power of the linear multivariate regression and the top performing machine learning model were compared using mean R2 measured in 30-fold cross validation. EXPOSURES County-level demographic factors (population; rural status; census region; and race/ethnicity, sex, and age composition), socioeconomic factors (median income, unemployment rate, and percentage of population with some college education), health care factors (rate of uninsured adults and primary care physicians), and environmental factors (access to healthy foods and access to exercise opportunities). MAIN OUTCOMES AND MEASURES County-level obesity prevalence in 2018, its association with each county-level factor, and the percentage of variation in county-level obesity prevalence explained by linear multivariate and gradient boosting machine regression measured with R2. RESULTS Among the 3138 counties studied, the mean (range) obesity prevalence was 31.5% (12.8%-47.8%). In multivariate regressions, demographic factors explained 44.9% of variation in obesity prevalence; socioeconomic factors, 33.0%; environmental factors, 15.5%; and health care factors, 9.1%. The county-level factors with the strongest association with obesity were census region, median household income, and percentage of population with some college education. R2 values of univariate regressions of obesity prevalence were 0.238 for census region, 0.218 for median household income, and 0.160 for percentage of population with some college education. Multivariate linear regression and gradient boosting machine regression (the best-performing machine learning model) of obesity prevalence using all county-level demographic, socioeconomic, health care, and environmental factors had R2 values of 0.58 and 0.66, respectively (P < .001). CONCLUSIONS AND RELEVANCE Obesity prevalence varies significantly between counties. County-level demographic, socioeconomic, health care, and environmental factors explain the majority of variation in county-level obesity prevalence. Using machine learning models may explain significantly more of the variation in obesity prevalence..
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Affiliation(s)
- David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California
- Department of Preoperative Services, Lucile Packard Children’s Hospital Stanford, Stanford, California
| | - Areli Valencia
- Medical Student, Stanford University School of Medicine, Stanford, California
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California
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Zhang B, Ren H, Huang G, Cheng Y, Hu C. Predicting blood pressure from physiological index data using the SVR algorithm. BMC Bioinformatics 2019; 20:109. [PMID: 30819090 PMCID: PMC6396542 DOI: 10.1186/s12859-019-2667-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 02/01/2019] [Indexed: 12/11/2022] Open
Abstract
Background Blood pressure diseases have increasingly been identified as among the main factors threatening human health. How to accurately and conveniently measure blood pressure is the key to the implementation of effective prevention and control measures for blood pressure diseases. Traditional blood pressure measurement methods exhibit many inherent disadvantages, for example, the time needed for each measurement is difficult to determine, continuous measurement causes discomfort, and the measurement process is relatively cumbersome. Wearable devices that enable continuous measurement of blood pressure provide new opportunities and hopes. Although machine learning methods for blood pressure prediction have been studied, the accuracy of the results does not satisfy the needs of practical applications. Results This paper proposes an efficient blood pressure prediction method based on the support vector machine regression (SVR) algorithm to solve the key gap between the need for continuous measurement for prophylaxis and the lack of an effective method for continuous measurement. The results of the algorithm were compared with those obtained from two classical machine learning algorithms, i.e., linear regression (LinearR), back propagation neural network (BP), with respect to six evaluation indexes (accuracy, pass rate, mean absolute percentage error (MAPE), mean absolute error (MAE), R-squared coefficient of determination (R2) and Spearman’s rank correlation coefficient). The experimental results showed that the SVR model can accurately and effectively predict blood pressure. Conclusion The multi-feature joint training and predicting techniques in machine learning can potentially complement and greatly improve the accuracy of traditional blood pressure measurement, resulting in better disease classification and more accurate clinical judgements.
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Affiliation(s)
- Bing Zhang
- School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.,The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Hebei Avenue, Qinhuangdao, 066004, China
| | - Huihui Ren
- School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China.,The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Hebei Avenue, Qinhuangdao, 066004, China
| | - Guoyan Huang
- School of Information Science and Engineering, Yanshan University, Hebei Avenue, Qinhuangdao, 066004, China. .,The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Hebei Avenue, Qinhuangdao, 066004, China.
| | - Yongqiang Cheng
- Department of Computer Science and Technology, University of Hull, Hull, HU6 7RX, UK
| | - Changzhen Hu
- Beijing Key Laboratory of Software Security Engineering Technique, Beijing Institute of Technology, Beijing, 100081, China
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Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8081325] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.
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Abstract
PURPOSE OF REVIEW Evidence that artificial intelligence (AI) is useful for predicting risk factors for hypertension and its management is emerging. However, we are far from harnessing the innovative AI tools to predict these risk factors for hypertension and applying them to personalized management. This review summarizes recent advances in the computer science and medical field, illustrating the innovative AI approach for potential prediction of early stages of hypertension. Additionally, we review ongoing research and future implications of AI in hypertension management and clinical trials, with an eye towards personalized medicine. RECENT FINDINGS Although recent studies demonstrate that AI in hypertension research is feasible and possibly useful, AI-informed care has yet to transform blood pressure (BP) control. This is due, in part, to lack of data on AI's consistency, accuracy, and reliability in the BP sphere. However, many factors contribute to poorly controlled BP, including biological, environmental, and lifestyle issues. AI allows insight into extrapolating data analytics to inform prescribers and patients about specific factors that may impact their BP control. To date, AI has been mainly used to investigate risk factors for hypertension, but has not yet been utilized for hypertension management due to the limitations of study design and of physician's engagement in computer science literature. The future of AI with more robust architecture using multi-omics approaches and wearable technology will likely be an important tool allowing to incorporate biological, lifestyle, and environmental factors into decision-making of appropriate drug use for BP control.
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Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2017:2187904. [PMID: 29317994 PMCID: PMC5727829 DOI: 10.1155/2017/2187904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/05/2017] [Accepted: 10/16/2017] [Indexed: 12/21/2022]
Abstract
Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination (R2) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R2 = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.
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Olivera AR, Roesler V, Iochpe C, Schmidt MI, Vigo Á, Barreto SM, Duncan BB. Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study. SAO PAULO MED J 2017; 135:234-246. [PMID: 28746659 PMCID: PMC10019841 DOI: 10.1590/1516-3180.2016.0309010217] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 02/01/2017] [Indexed: 01/23/2023] Open
Abstract
CONTEXT AND OBJECTIVE: Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. DESIGN AND SETTING: Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. METHODS: After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. RESULTS: The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. CONCLUSION: Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.
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Affiliation(s)
- André Rodrigues Olivera
- MSc. IT Analyst, Postgraduate Computing Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
| | - Valter Roesler
- PhD. Professor, Postgraduate Computing Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
| | - Cirano Iochpe
- PhD. Professor, Postgraduate Computing Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
| | - Maria Inês Schmidt
- PhD. Professor, Postgraduate Epidemiology Program and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
| | - Álvaro Vigo
- PhD. Professor, Postgraduate Epidemiology Program, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
| | - Sandhi Maria Barreto
- PhD. Professor, Department of Social and Preventive Medicine & Postgraduate Program in Public Health, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte (MG), Brazil.
| | - Bruce Bartholow Duncan
- PhD. Professor, Postgraduate Epidemiology Program and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre (RS), Brazil.
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Abstract
This paper presents a new study based on a machine learning technique, specifically an artificial neural network, for predicting systolic blood pressure through the correlation of variables (age, BMI, exercise level, alcohol consumption level, smoking status, stress level, and salt intake level). The study was carried out using a database containing a variety of variables/factors. Each database of raw data was split into two parts: one part for training the neural network and the remaining part for testing the performance of the network. Two neural network algorithms, back-propagation and radial basis function, were used to construct and validate the prediction system. According to the experiment, the accuracy of our predictions of systolic blood pressure values exceeded 90%. Our experimental results show that artificial neural networks are suitable for modeling and predicting systolic blood pressure. This new method of predicting systolic blood pressure helps to give an early warning to adults, who may not get regular blood pressure measurements that their blood pressure might be at an unhealthy level. Also, because an isolated measurement of blood pressure is not always very accurate due to daily fluctuations, our predictor can provide the predicted value as another figure for medical staff to refer to.
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Affiliation(s)
| | - Hao Wu
- The University of Hong Kong, Hong Kong
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Golino HF, Amaral LSDB, Duarte SFP, Gomes CMA, Soares TDJ, dos Reis LA, Santos J. Predicting increased blood pressure using machine learning. J Obes 2014; 2014:637635. [PMID: 24669313 PMCID: PMC3941962 DOI: 10.1155/2014/637635] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Revised: 10/12/2013] [Accepted: 11/16/2013] [Indexed: 01/21/2023] Open
Abstract
The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.
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Affiliation(s)
- Hudson Fernandes Golino
- Laboratório de Investigação da Arquitetura Cognitiva, Universidade Federal de Minas Gerais, 30000-000 Belo Horizonte, Minas Gerais, MG, Brazil
- *Hudson Fernandes Golino:
| | | | - Stenio Fernando Pimentel Duarte
- Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil
| | - Cristiano Mauro Assis Gomes
- Laboratório de Investigação da Arquitetura Cognitiva, Universidade Federal de Minas Gerais, 30000-000 Belo Horizonte, Minas Gerais, MG, Brazil
| | - Telma de Jesus Soares
- Instituto Multidisciplinar de Saúde, Universidade Federal da Bahia, 40000-000 Bahia, BA, Brazil
| | - Luciana Araujo dos Reis
- Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil
| | - Joselito Santos
- Núcleo de Pós-Graduação, Pesquisa e Extenção, Faculdade Independente do Nordeste, São Luís Avenue, 1305, 45000-000 Candeias, Vitória da Conquista, BA, Brazil
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