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Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatr 2023; 23:841. [PMID: 38087195 PMCID: PMC10717316 DOI: 10.1186/s12877-023-04477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area. METHODS This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used. RESULTS A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data. CONCLUSION The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.
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Affiliation(s)
- Ayushi Das
- International Institute for Population Sciences, Deonar, Mumbai, 400088, India
| | - Preeti Dhillon
- Department of Survey Research and Data Analytics, International Institute for Population Sciences, Deonar, Mumbai, 400088, India.
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Moore JS, Nesbit MA, Moore T. Appraisal of Cardiovascular Risk Factors, Biomarkers, and Ocular Imaging in Cardiovascular Risk Prediction. Curr Cardiol Rev 2023; 19:72-81. [PMID: 37497700 PMCID: PMC10636798 DOI: 10.2174/1573403x19666230727101926] [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: 09/30/2022] [Revised: 05/12/2023] [Accepted: 06/15/2023] [Indexed: 07/28/2023] Open
Abstract
Cardiovascular disease remains a leading cause of death worldwide despite the use of available cardiovascular disease risk prediction tools. Identification of high-risk individuals via risk stratification and screening at sub-clinical stages, which may be offered by ocular screening, is important to prevent major adverse cardiac events. Retinal microvasculature has been widely researched for potential application in both diabetes and cardiovascular disease risk prediction. However, the conjunctival microvasculature as a tool for cardiovascular disease risk prediction remains largely unexplored. The purpose of this review is to evaluate the current cardiovascular risk assessment methods, identifying gaps in the literature that imaging of the ocular microcirculation may have the potential to fill. This review also explores the themes of machine learning, risk scores, biomarkers, medical imaging, and clinical risk factors. Cardiovascular risk classification varies based on the population assessed, the risk factors included, and the assessment methods. A more tailored, standardised and feasible approach to cardiovascular risk prediction that utilises technological and medical imaging advances, which may be offered by ocular imaging, is required to support cardiovascular disease prevention strategies and clinical guidelines.
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Affiliation(s)
- Julie S. Moore
- School of Biomedical Sciences, Ulster University, York St, Belfast BT15 1ED, United Kingdom
- Integrated Diagnostics Laboratory, Ulster University, 3-5a Frederick St, Belfast, Northern Ireland, United Kingdom
| | - M. Andrew Nesbit
- School of Biomedical Sciences, Ulster University, York St, Belfast BT15 1ED, United Kingdom
- Integrated Diagnostics Laboratory, Ulster University, 3-5a Frederick St, Belfast, Northern Ireland, United Kingdom
| | - Tara Moore
- School of Biomedical Sciences, Ulster University, York St, Belfast BT15 1ED, United Kingdom
- Integrated Diagnostics Laboratory, Ulster University, 3-5a Frederick St, Belfast, Northern Ireland, United Kingdom
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Li JX, Li L, Zhong X, Fan SJ, Cen T, Wang J, He C, Zhang Z, Luo YN, Liu XX, Hu LX, Zhang YD, Qiu HL, Dong GH, Zou XG, Yang BY. Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS). Glob Health Res Policy 2022; 7:48. [PMID: 36474302 PMCID: PMC9724436 DOI: 10.1186/s41256-022-00282-y] [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: 09/19/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region. METHODS A total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis. Sixteen candidate factors, including seven demographic factors, 4 lifestyle factors, and 5 clinical factors, were collected from a questionnaire and health examination records. CVD was defined according to International Clinical Diagnosis (ICD-10) codes. We first used logistic regression models to investigate the association between each of the candidate factors and CVD. Then, we employed 3 machine learning methods-Random Forest, Random Ferns, and Extreme Gradient Boosting-to rank and identify prominent factors associated with CVD. Stratification analyses by sex, ethnicity, education level, economic status, and residential setting were also performed to test the consistency of the ranking. RESULTS The prevalence of CVD in Kashgar prefecture was 8.1%. All the 16 candidate factors were confirmed to be significantly associated with CVD (odds ratios ranged from 1.03 to 2.99, all p values < 0.05) in logistic regression models. Further machine learning-based analysis suggested that age, occupation, hypertension, exercise frequency, and dietary pattern were the five most prominent factors associated with CVD. The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample. CONCLUSIONS CVD is a major public health concern in Kashgar prefecture. Age, occupation, hypertension, exercise frequency, and dietary pattern might be the prominent factors associated with CVD in this region.In the future, these factors should be given priority in preventing CVD in future.
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Affiliation(s)
- Jia-Xin Li
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Li Li
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Xuemei Zhong
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Shu-Jun Fan
- grid.508371.80000 0004 1774 3337Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Tao Cen
- grid.284723.80000 0000 8877 7471Department of Research and Development, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Jianquan Wang
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Chuanjiang He
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Zhoubin Zhang
- grid.508371.80000 0004 1774 3337Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Ya-Na Luo
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Xiao-Xuan Liu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Li-Xin Hu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Yi-Dan Zhang
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Hui-Ling Qiu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Guang-Hui Dong
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Xiao-Guang Zou
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Bo-Yi Yang
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
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Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5475313. [PMID: 35602638 PMCID: PMC9119773 DOI: 10.1155/2022/5475313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/26/2022] [Indexed: 11/18/2022]
Abstract
Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.
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