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Prajapati SK, Pathak A, Samaiya PK. Alzheimer's disease: from early pathogenesis to novel therapeutic approaches. Metab Brain Dis 2024; 39:1231-1254. [PMID: 39046584 DOI: 10.1007/s11011-024-01389-6] [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: 12/06/2023] [Accepted: 07/15/2024] [Indexed: 07/25/2024]
Abstract
The mainstay behind Alzheimer's disease (AD) remains unknown due to the elusive pathophysiology of the disease. Beta-amyloid and phosphorylated Tau is still widely incorporated in various research studies while studying AD. However, they are not sufficient. Therefore, many scientists and researchers have dug into AD studies to deliver many innovations in this field. Many novel biomarkers, such as phosphoglycerate-dehydrogenase, clusterin, microRNA, and a new peptide ratio (Aβ37/Aβ42) in cerebral-spinal fluid, plasma glial-fibrillary-acidic-protein, and lipid peroxidation biomarkers, are mushrooming. They are helping scientists find breakthroughs and substantiating their research on the early detection of AD. Neurovascular unit dysfunction in AD is a significant discovery that can help us understand the relationship between neuronal activity and cerebral blood flow. These new biomarkers are promising and can take these AD studies to another level. There have also been big steps forward in diagnosing and finding AD. One example is self-administered-gerocognitive-examination, which is less expensive and better at finding AD early on than mini-mental-state-examination. Quantum brain sensors and electrochemical biosensors are innovations in the detection field that must be explored and incorporated into the studies. Finally, novel innovations in AD studies like nanotheranostics are the future of AD treatment, which can not only diagnose and detect AD but also offer treatment. Non-pharmacological strategies to treat AD have also yielded interesting results. Our literature review spans from 1957 to 2022, capturing research and trends in the field over six decades. This review article is an update not only on the recent advances in the search for credible biomarkers but also on the newer detection techniques and therapeutic approaches targeting AD.
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Affiliation(s)
- Santosh Kumar Prajapati
- Bhavdiya Institute of Pharmaceutical Sciences and Research, Ayodhya, UP, India
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL, 33613, USA
| | - Arjit Pathak
- Department of Pharmacy Shri G.S. Institute of Technology and Science, Indore, 452003, Madhya Pradesh, India
| | - Puneet K Samaiya
- Department of Pharmacy Shri G.S. Institute of Technology and Science, Indore, 452003, Madhya Pradesh, India.
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Javaid H, Nouman M, Cheaha D, Kumarnsit E, Chatpun S. Complexity measures reveal age-dependent changes in electroencephalogram during working memory task. Behav Brain Res 2024; 470:115070. [PMID: 38806100 DOI: 10.1016/j.bbr.2024.115070] [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: 12/11/2023] [Revised: 05/09/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.
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Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, Ex4 4QG, United Kingdom
| | - Muhammad Nouman
- Sirindhorn School of Prosthetics and Orthotics, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Dania Cheaha
- Biology program, Division of Biological Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand
| | - Ekkasit Kumarnsit
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
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Wang X, Zhou S, Ye N, Li Y, Zhou P, Chen G, Hu H. Predictive models of Alzheimer's disease dementia risk in older adults with mild cognitive impairment: a systematic review and critical appraisal. BMC Geriatr 2024; 24:531. [PMID: 38898411 PMCID: PMC11188292 DOI: 10.1186/s12877-024-05044-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 05/06/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Mild cognitive impairment has received widespread attention as a high-risk population for Alzheimer's disease, and many studies have developed or validated predictive models to assess it. However, the performance of the model development remains unknown. OBJECTIVE The objective of this review was to provide an overview of prediction models for the risk of Alzheimer's disease dementia in older adults with mild cognitive impairment. METHOD PubMed, EMBASE, Web of Science, and MEDLINE were systematically searched up to October 19, 2023. We included cohort studies in which risk prediction models for Alzheimer's disease dementia in older adults with mild cognitive impairment were developed or validated. The Predictive Model Risk of Bias Assessment Tool (PROBAST) was employed to assess model bias and applicability. Random-effects models combined model AUCs and calculated (approximate) 95% prediction intervals for estimations. Heterogeneity across studies was evaluated using the I2 statistic, and subgroup analyses were conducted to investigate sources of heterogeneity. Additionally, funnel plot analysis was utilized to identify publication bias. RESULTS The analysis included 16 studies involving 9290 participants. Frequency analysis of predictors showed that 14 appeared at least twice and more, with age, functional activities questionnaire, and Mini-mental State Examination scores of cognitive functioning being the most common predictors. From the studies, only two models were externally validated. Eleven studies ultimately used machine learning, and four used traditional modelling methods. However, we found that in many of the studies, there were problems with insufficient sample sizes, missing important methodological information, lack of model presentation, and all of the models were rated as having a high or unclear risk of bias. The average AUC of the 15 best-developed predictive models was 0.87 (95% CI: 0.83, 0.90). DISCUSSION Most published predictive modelling studies are deficient in rigour, resulting in a high risk of bias. Upcoming research should concentrate on enhancing methodological rigour and conducting external validation of models predicting Alzheimer's disease dementia. We also emphasize the importance of following the scientific method and transparent reporting to improve the accuracy, generalizability and reproducibility of study results. REGISTRATION This systematic review was registered in PROSPERO (Registration ID: CRD42023468780).
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Affiliation(s)
- Xiaotong Wang
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Shi Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Niansi Ye
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Yucan Li
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Pengjun Zhou
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Gao Chen
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Hui Hu
- College of Nursing, Hubei University of Chinese Medicine, Wuhan, China.
- Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Wuhan, China.
- Hubei Shizhen Laboratory, Wuhan, China.
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Tsiakiri A, Bakirtzis C, Plakias S, Vlotinou P, Vadikolias K, Terzoudi A, Christidi F. Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data. Biomedicines 2024; 12:1232. [PMID: 38927439 PMCID: PMC11201179 DOI: 10.3390/biomedicines12061232] [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: 04/27/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Neurocognitive disorders (NCDs) are progressive conditions that severely impact cognitive function and daily living. Understanding the transition from mild to major NCD is crucial for personalized early intervention and effective management. Predictive models incorporating demographic variables, clinical data, and scores on neuropsychological and emotional tests can significantly enhance early detection and intervention strategies in primary healthcare settings. We aimed to develop and validate predictive models for the progression from mild NCD to major NCD using demographic, clinical, and neuropsychological data from 132 participants over a two-year period. Generalized Estimating Equations were employed for data analysis. Our final model achieved an accuracy of 83.7%. A higher body mass index and alcohol drinking increased the risk of progression from mild NCD to major NCD, while female sex, higher praxis abilities, and a higher score on the Geriatric Depression Scale reduced the risk. Here, we show that integrating multiple factors-ones that can be easily examined in clinical settings-into predictive models can improve early diagnosis of major NCD. This approach could facilitate timely interventions, potentially mitigating the progression of cognitive decline and improving patient outcomes in primary healthcare settings. Further research should focus on validating these models across diverse populations and exploring their implementation in various clinical contexts.
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Affiliation(s)
- Anna Tsiakiri
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
| | - Christos Bakirtzis
- B’ Department of Neurology and the MS Center, School of Medicine, AHEPA University Hospital, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Spyridon Plakias
- Department of Physical Education and Sport Science, University of Thessaly, 41500 Trikala, Greece;
| | - Pinelopi Vlotinou
- Department of Occupational Therapy, University of West Attica, 12243 Athens, Greece;
| | - Konstantinos Vadikolias
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
| | - Aikaterini Terzoudi
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
| | - Foteini Christidi
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
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Maimaitituerxun R, Chen W, Xiang J, Xie Y, Xiao F, Wu XY, Chen L, Yang J, Liu A, Dai W. Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis. Brain Behav 2024; 14:e3456. [PMID: 38450963 PMCID: PMC10918605 DOI: 10.1002/brb3.3456] [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: 10/30/2023] [Revised: 02/04/2024] [Accepted: 02/10/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND As the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self-care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health-related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach. METHODS This hospital-based case-control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM-related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi-square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison. RESULTS A total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2 ± 10.3 and 60.4 ± 9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p > .05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC = .75 [95% confidence interval (CI): .71-.78] and .67 [95% CI: .61-.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC = .76 [95% CI: .72-.80] and .69 [95% CI: .62-.75] in the training and validation sets, respectively). CONCLUSION The established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice.
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Affiliation(s)
- Rehanguli Maimaitituerxun
- Department of Epidemiology and Health Statistics, Xiangya School of Public HealthCentral South UniversityChangshaHunanChina
| | - Wenhang Chen
- Department of NephrologyXiangya Hospital, Central South UniversityChangshaHunanChina
| | - Jingsha Xiang
- Department of Human ResourcesJinan Central Hospital Affiliated to Shandong First Medical UniversityJinanShandongChina
| | - Yu Xie
- Department of Epidemiology and Health Statistics, Xiangya School of Public HealthCentral South UniversityChangshaHunanChina
| | - Fang Xiao
- Department of Toxicology, Xiangya School of Public HealthCentral South UniversityChangshaHunanChina
| | - Xin Yin Wu
- Department of Epidemiology and Health Statistics, Xiangya School of Public HealthCentral South UniversityChangshaHunanChina
| | - Letao Chen
- Infection Control CenterXiangya Hospital, Central South UniversityChangshaHunanChina
| | - Jianzhou Yang
- Department of Preventive MedicineChangzhi Medical CollegeChangzhiShanxiChina
| | - Aizhong Liu
- Department of Epidemiology and Health Statistics, Xiangya School of Public HealthCentral South UniversityChangshaHunanChina
| | - Wenjie Dai
- Department of Epidemiology and Health Statistics, Xiangya School of Public HealthCentral South UniversityChangshaHunanChina
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Wahbeh F, Restifo D, Laws S, Pawar A, Parikh NS. Impact of tobacco smoking on disease-specific outcomes in common neurological disorders: A scoping review. J Clin Neurosci 2024; 122:10-18. [PMID: 38428126 DOI: 10.1016/j.jocn.2024.02.013] [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: 11/06/2023] [Revised: 01/27/2024] [Accepted: 02/12/2024] [Indexed: 03/03/2024]
Abstract
Although the association of smoking with the risk of incident neurological disorders is well established, less is known about the impact of smoking and smoking cessation on outcomes of these conditions. The objective of this scoping review was to synthesize what is known about the impact of smoking and smoking cessation on disease-specific outcomes for seven common neurological disorders. We included 67 studies on the association of smoking and smoking cessation on disease-specific outcomes. For multiple sclerosis, smoking was associated with greater clinical and radiological disease progression, relapses, risk for disease-related death, cognitive decline, and mood symptoms, in addition to reduced treatment effectiveness. For stroke and transient ischemic attack, smoking was associated with greater rates of stroke recurrence, post-stroke cardiovascular outcomes, post-stroke mortality, post-stroke cognitive impairment, and functional impairment. In patients with cognitive impairment and dementia, smoking was associated with faster cognitive decline, and smoking was also associated with greater cognitive decline in Parkinson's disease, but not motor symptom worsening. Patients with amyotrophic lateral sclerosis who smoked faced increased mortality. Last, in patients with cluster headache, smoking was associated with more frequent and longer cluster attack periods. Conversely, for multiple sclerosis and stroke, smoking cessation was associated with improved disease-specific outcomes. In summary, whereas smoking is detrimentally associated with disease-specific outcomes in common neurological conditions, there is growing evidence that smoking cessation may improve outcomes. Effective smoking cessation interventions should be leveraged in the management of common neurological disorders to improve patient outcomes.
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Affiliation(s)
- Farah Wahbeh
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Daniel Restifo
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Sa'ad Laws
- Education and Research, Health Sciences Library, Weill Cornell Medicine - Qatar, Doha, Qatar
| | - Anokhi Pawar
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, NY, USA
| | - Neal S Parikh
- Clinical and Translational Neuroscience Unit, Feil Family Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medicine, New York, NY, USA.
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Zhang P, Wu L, Zou TT, Zou Z, Tu J, Gong R, Kuang J. Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study. JMIR Form Res 2024; 8:e48487. [PMID: 38170581 PMCID: PMC10794958 DOI: 10.2196/48487] [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: 04/25/2023] [Revised: 08/29/2023] [Accepted: 09/15/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking. OBJECTIVE This study aimed to develop machine learning-based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI. METHODS A total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models-artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest-were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score. RESULTS In total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease. CONCLUSIONS The ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning-based prediction models may improve patient management and outcomes in clinical practice.
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Affiliation(s)
- Pin Zhang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
- School of Public Health and Management, Nanchang Medical College, Nanchang, China
| | - Lei Wu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
| | - Ting-Ting Zou
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
| | - ZiXuan Zou
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
| | - JiaXin Tu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
| | - Ren Gong
- Department of Cardiology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jie Kuang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China
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Zhang Y, Li Y, Song S, Li Z, Lu M, Shan G. Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models. J Alzheimers Dis 2024; 101:147-157. [PMID: 39121117 DOI: 10.3233/jad-240285] [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: 08/11/2024]
Abstract
Background Mild cognitive impairment (MCI) patients are at a high risk of developing Alzheimer's disease and related dementias (ADRD) at an estimated annual rate above 10%. It is clinically and practically important to accurately predict MCI-to-dementia conversion time. Objective It is clinically and practically important to accurately predict MCI-to-dementia conversion time by using easily available clinical data. Methods The dementia diagnosis often falls between two clinical visits, and such survival outcome is known as interval-censored data. We utilized the semi-parametric model and the random forest model for interval-censored data in conjunction with a variable selection approach to select important measures for predicting the conversion time from MCI to dementia. Two large AD cohort data sets were used to build, validate, and test the predictive model. Results We found that the semi-parametric model can improve the prediction of the conversion time for patients with MCI-to-dementia conversion, and it also has good predictive performance for all patients. Conclusions Interval-censored data should be analyzed by using the models that were developed for interval- censored data to improve the model performance.
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Affiliation(s)
- Yahui Zhang
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Yulin Li
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Shangchen Song
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Zhigang Li
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Minggen Lu
- School of Community Health Sciences, University of Nevada, Reno, NV, USA
| | - Guogen Shan
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
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Tsiaras Y, Kiosseoglou G, Dardiotis E, Yannakoulia M, Hadjigeorgiou GM, Sakka P, Ntanasi E, Scarmeas N, Kosmidis MH. Predictive ability of the clock drawing test to detect mild cognitive impairment and dementia over time: Results from the HELIAD study. Clin Neuropsychol 2023; 37:1651-1668. [PMID: 36645823 DOI: 10.1080/13854046.2023.2167736] [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: 05/31/2022] [Accepted: 01/06/2023] [Indexed: 01/18/2023]
Abstract
Objective: We investigated the diagnostic accuracy of the Clock Drawing Test (CDT) in discriminating Mild Cognitive Impairment (MCI) and dementia from normal cognition. Additionally, its clinical utility in predicting the transition from normal cognition to MCI and dementia over the course of several years was explored. Method: In total, 1037 older adults (633 women) who completed the CDT in a baseline assessment were drawn from the population-based HELIAD cohort. Among these, 848 participants were identified as cognitively normal, 142 as having MCI and 47 with dementia during the baseline assessment. Of these individuals, 565 attended the follow-up assessment (mean interval: 3.21 years). ROC curve and binary logistic regression analyses were performed. Results: The CDT exhibited good diagnostic accuracy for the discrimination between dementia and normal cognition (AUC = .879, SN = .813, SP = .778, LR+ = 3.66, LR- = .240, < .001, d = 1.655) and acceptable diagnostic accuracy for the discrimination between dementia and MCI (AUC=.761, SN= .750, SP= .689, LR+ = 2.41, LR- = .362, p < .001, d = 1.003). We found limited diagnostic accuracy, however, for the discrimination between MCI and normal cognition (AUC = .686, SN = .764, SP = .502, LR+ = 1.53, LR- = .470, p < .001, d = .685). Moreover, the CDT significantly predicted the transition from normal cognition to dementia [Exp(B)= 1.257, p = .022], as well as the transition from MCI to normal cognition [Exp(B) = 1.334, p = .023] during the longitudinal investigation. Conclusions: The CDT is a neuropsychological test with acceptable diagnostic accuracy for the discrimination of dementia from MCI and normal cognition. Furthermore, it has an important predictive value for the transition from normal cognition to dementia and from MCI to normal cognition.
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Affiliation(s)
- Yiannis Tsiaras
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Psychiatric Department, 424 General Military Hospital, Thessaloniki, Greece
| | - Grigoris Kiosseoglou
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efthimios Dardiotis
- Department of Neurology, Faculty of Medicine, University of Thessaly, Volos, Greece
| | - Mary Yannakoulia
- Department of Nutrition and-Dietetics, Harokopio University, Kallithea, Greece
| | | | - Paraskevi Sakka
- Athens Association of Alzheimer's Disease and Related Disorders, Athens, Greece
| | - Eva Ntanasi
- Athens Association of Alzheimer's Disease and Related Disorders, Athens, Greece
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
- Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Τhe Gertrude H. Sergievsky Center, Department of Neurology, Columbia University, New York, ΝΥ, USA
| | - Mary H Kosmidis
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Amin E, Elgammal YM, Zahran MA, Abdelsalam MM. Alzheimer's disease: new insight in assessing of amyloid plaques morphologies using multifractal geometry based on Naive Bayes optimized by random forest algorithm. Sci Rep 2023; 13:18568. [PMID: 37903890 PMCID: PMC10616199 DOI: 10.1038/s41598-023-45972-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 10/26/2023] [Indexed: 11/01/2023] Open
Abstract
Alzheimer's disease (AD) is a physical illness, which damages a person's brain; it is the most common cause of dementia. AD can be characterized by the formation of amyloid-beta (Aβ) deposits. They exhibit diverse morphologies that range from diffuse to dense-core plaques. Most of the histological images cannot be described precisely by traditional geometry or methods. Therefore, this study aims to employ multifractal geometry in assessing and classifying amyloid plaque morphologies. The classification process is based on extracting the most descriptive features related to the amyloid-beta (Aβ) deposits using the Naive Bayes classifier. To eliminate the less important features, the Random Forest algorithm has been used. The proposed methodology has achieved an accuracy of 99%, sensitivity of 100%, and specificity of 98.5%. This study employed a new dataset that had not been widely used before.
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Affiliation(s)
- Elshaimaa Amin
- Future Higher Institute of Engineering and Technology, Mansoura, Egypt
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Yasmina M Elgammal
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - M A Zahran
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Mohamed M Abdelsalam
- Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
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11
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Ahn K, Cho M, Kim SW, Lee KE, Song Y, Yoo S, Jeon SY, Kim JL, Yoon DH, Kong HJ. Deep Learning of Speech Data for Early Detection of Alzheimer's Disease in the Elderly. Bioengineering (Basel) 2023; 10:1093. [PMID: 37760195 PMCID: PMC10525115 DOI: 10.3390/bioengineering10091093] [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: 06/04/2023] [Revised: 08/16/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. OBJECTIVE Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. MATERIALS AND METHODS The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results. RESULTS Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group. CONCLUSIONS The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.
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Affiliation(s)
- Kichan Ahn
- Interdisciplinary Program in Medical Informatics Major, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Minwoo Cho
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea;
- Medical Big Data Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; (S.W.K.); (K.E.L.)
- Department of Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Suk Wha Kim
- Medical Big Data Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; (S.W.K.); (K.E.L.)
- Department of Plastic Surgery and Institute of Aesthetic Medicine, CHA Bundang Medical Center, CHA University, Seongnam 13496, Republic of Korea
| | - Kyu Eun Lee
- Medical Big Data Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; (S.W.K.); (K.E.L.)
- Department of Surgery, Seoul National University Hospital and College of Medicine, Seoul 03080, Republic of Korea
| | - Yoojin Song
- Department of Psychiatry, Kangwon National University, Chuncheon 24289, Republic of Korea;
| | - Seok Yoo
- Unidocs Inc., Seoul 03080, Republic of Korea;
| | - So Yeon Jeon
- Department of Psychiatry, Chungnam National University Hospital, Daejeon 30530, Republic of Korea; (S.Y.J.); (J.L.K.)
| | - Jeong Lan Kim
- Department of Psychiatry, Chungnam National University Hospital, Daejeon 30530, Republic of Korea; (S.Y.J.); (J.L.K.)
- Department of Psychiatry, Chungnam National University College of Medicine, Daejeon 30530, Republic of Korea
| | - Dae Hyun Yoon
- Department of Psychiatry, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul 03080, Republic of Korea;
| | - Hyoun-Joong Kong
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea;
- Medical Big Data Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; (S.W.K.); (K.E.L.)
- Department of Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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12
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Long Z, Li J, Fan J, Li B, Du Y, Qiu S, Miao J, Chen J, Yin J, Jing B. Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics. Front Aging Neurosci 2023; 15:1212275. [PMID: 37719872 PMCID: PMC10501142 DOI: 10.3389/fnagi.2023.1212275] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/21/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently. Methods In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance. Results The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus. Conclusion Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.
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Affiliation(s)
- Zhuqing Long
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Jie Li
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Jianghua Fan
- Department of Pediatric Emergency Center, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Bo Li
- Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Yukeng Du
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Shuang Qiu
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Jichang Miao
- Department of Medical Devices, Nanfang Hospital, Guangzhou, China
| | - Jian Chen
- School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, China
| | - Juanwu Yin
- Medical Apparatus and Equipment Deployment, Hunan Children’s Hospital, Changsha, Hunan Province, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing, China
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Yang L, He L, Bu Z, Xuan C, Yu C, Wu J. Serum Protein-Based Profiles for the Diagnostic Model of Alzheimer's Disease. Am J Alzheimers Dis Other Demen 2023; 38:15333175231220166. [PMID: 38041525 PMCID: PMC10693785 DOI: 10.1177/15333175231220166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2023]
Abstract
BACKGROUND Determining a non-invasive, serum-based diagnostic panel for early diagnosis of AD will play a significant role in the prevention and treatment of the disease. METHODS We performed standardized clinical assessments and neuroimaging measurements in 45 patients with AD and an equal number of sex - and age-matched controls. 48 target peptides of 14 identified target proteins were quantitatively analyzed by PRM. RESULTS 8 protein markers were screened, including SAA4, PPBP, PF4, APOA4, F10, CPB2, C1S and IGHM. An diagnosis panel including 8 proteins and demographic characteristics markers respectively was found to be the robust with a AUC of 92.3%. CONCLUSIONS Our study developed a new panel including protein and demographic characteristics that could be used to distinguish AD from control candidates.
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Affiliation(s)
- Li Yang
- Zhejiang Hospital, Hangzhou, China
| | - Liang He
- Disease Control and Prevention Center of Zhuji, Zhuji, China
| | | | - Cheng Xuan
- Zhuji Second People’s Hospital, Zhuji, China
| | - Caiyan Yu
- Zhuji Second People’s Hospital, Zhuji, China
| | - Jiong Wu
- Zhejiang Hospital, Hangzhou, China
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14
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Elgammal YM, Zahran MA, Abdelsalam MM. A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm. Sci Rep 2022; 12:22381. [PMID: 36572791 PMCID: PMC9792538 DOI: 10.1038/s41598-022-26958-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 12/22/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's Disease (AD) is considered one of the most diseases that much prevalent among elderly people all over the world. AD is an incurable neurodegenerative disease affecting cognitive functions and were characterized by progressive and collective functions deteriorating. Remarkably, early detection of AD is essential for the development of new and invented treatment strategies. As Dementia causes irreversible damage to the brain neurons and leads to changes in its structure that can be described adequately within the framework of multifractals. Hence, the present work focus on developing a promising and efficient computing technique to pre-process and classify the AD disease especially in the early stages using multifractal geometry to extract the most changeable features due to AD. Then, A machine learning classification algorithm (K-Nearest Neighbor) has been implemented in order to classify and detect the main four early stages of AD. Two datasets have been used to ensure the validation of the proposed methodology. The proposed technique has achieved 99.4% accuracy and 100% sensitivity. The comparative results show that the proposed classification technique outperforms is recent techniques in terms of performance measures.
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Affiliation(s)
- Yasmina M Elgammal
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - M A Zahran
- Theoretical Physics Group, Physics Department, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - Mohamed M Abdelsalam
- Computers Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
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15
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Shang Q, Zhang Q, Liu X, Zhu L. Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3144035. [PMID: 35572832 PMCID: PMC9106502 DOI: 10.1155/2022/3144035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/06/2022] [Accepted: 04/15/2022] [Indexed: 11/17/2022]
Abstract
This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer's disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer's disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group (P < 0.01). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group (P < 0.001). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score (R 2 = 0.1702, 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; P < 0.001). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model (P < 0.01). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model (P < 0.01). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.
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Affiliation(s)
- Qun Shang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Qi Zhang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Xiao Liu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Lingchen Zhu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
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16
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Issitt RW, Cortina-Borja M, Bryant W, Bowyer S, Taylor AM, Sebire N. Classification Performance of Neural Networks Versus Logistic Regression Models: Evidence From Healthcare Practice. Cureus 2022; 14:e22443. [PMID: 35345728 PMCID: PMC8942139 DOI: 10.7759/cureus.22443] [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] [Accepted: 02/20/2022] [Indexed: 12/19/2022] Open
Abstract
Machine learning encompasses statistical approaches such as logistic regression (LR) through to more computationally complex models such as neural networks (NN). The aim of this study is to review current published evidence for performance from studies directly comparing logistic regression, and neural network classification approaches in medicine. A literature review was carried out to identify primary research studies which provided information regarding comparative area under the curve (AUC) values for the overall performance of both LR and NN for a defined clinical healthcare-related problem. Following an initial search, articles were reviewed to remove those that did not meet the criteria and performance metrics were extracted from the included articles. Teh initial search revealed 114 articles; 21 studies were included in the study. In 13/21 (62%) of cases, NN had a greater AUC compared to LR, but in most the difference was small and unlikely to be of clinical significance; (unweighted mean difference in AUC 0.03 (95% CI 0-0.06) in favour of NN versus LR. In the majority of cases examined across a range of clinical settings, LR models provide reasonable performance that is only marginally improved using more complex methods such as NN. In many circumstances, the use of a relatively simple LR model is likely to be adequate for real-world needs but in specific circumstances in which large amounts of data are available, and where even small increases in performance would provide significant management value, the application of advanced analytic tools such as NNs may be indicated.
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Affiliation(s)
- Richard W Issitt
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Mario Cortina-Borja
- Statistics, Great Ormond Street Institute of Child Health, University College London (UCL), London, GBR
| | - William Bryant
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Stuart Bowyer
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Andrew M Taylor
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
| | - Neil Sebire
- Clinical Informatics, Great Ormond Street Hospital, National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) University College London (UCL), London, GBR
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Ahsan MM, Luna SA, Siddique Z. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare (Basel) 2022; 10:541. [PMID: 35327018 PMCID: PMC8950225 DOI: 10.3390/healthcare10030541] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/06/2023] Open
Abstract
Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.
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Affiliation(s)
- Md Manjurul Ahsan
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Shahana Akter Luna
- Medicine & Surgery, Dhaka Medical College & Hospital, Dhaka 1000, Bangladesh;
| | - Zahed Siddique
- Department of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA;
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Liu Z, Maiti T, Bender AR. A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer's Disease. J Alzheimers Dis 2021; 83:1859-1875. [PMID: 34459391 DOI: 10.3233/jad-201398] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
BACKGROUND The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection. OBJECTIVE The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia. METHODS We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia. RESULTS The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques. CONCLUSION These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers.
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Affiliation(s)
- Zihuan Liu
- Department of Statistics, Michigan State University, East Lansing, MI, USA
| | - Tapabrata Maiti
- Department of Statistics, Michigan State University, East Lansing, MI, USA
| | - Andrew R Bender
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, USA
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