Table 3.
Summary of studies
| SL. No | Author | Year published | Objective | Data set | Outcome | ML methods | Findings | Model performance |
|---|---|---|---|---|---|---|---|---|
| 1 | Kim et al. [15] | 2021 |
1. To compare models employing AI and traditional statistical methods in biological age (BA) prediction using clinical biomarkers 2. To compare the accuracy of BA prediction between various AI models 3. To compare the influence of each clinical biomarker on BA prediction between traditional and AI methods |
116,829 subjects aged 20 or older, who received routine health check-ups from 2015 through 2017 community hospitals in Korea | Biological Age |
Traditional methods—Linear Regression, 2nd polynomial regression AI, ML methods- XGB regression, RF regression, Support vector regression, Deep neural network |
AI models (mainly DNN) produced about 1.6 times stronger linear relationship on average than statistical models, hence outperforming traditional statistical methods in predicting biological age | R- squared value: Linear = 0.84, Polynomial = 0.99, XGBoost = 0.76, Random forest = 0.97, SVR = 0.88, DNN = 0.1 |
| 2 | Caballero et al. [17] | 2017 | To create a health score that can be compared across different waves in a longitudinal study, using anchor items and items that vary acorss waves | 17,886 subjects from first six ELSA waves | Healthy Ageing/health status |
Bayesian multilevel Item Response Theory approach ML methods- Decision Tree, Random Forest |
A combination of 2 data anlaytical methdologies was applied to create a measure of health in a cohort, which can be used to understand the ageing process. The metric includes a set of diferent functioning, mobility, sensorineural, cognitive, emotional aspects is feasible and psychometrically sound | NA |
| 3 | Qin et al. [18] | 2020 | To investigate the predictors of the health conditions of older people using machine learning methods | 29,477 combined dataset of 2013 & 2015 China Health and Retirement Longitudinal Survey (CHARLS) | Healthy Ageing/health status | Feature selection- Maximal Information Coefficient (MIC), pearson correlation coefficient ML methods- Linear regression, k-nearest neighbors(kNN), XGBoost, Decision Tree, Support Vector Machine, Artificial Neural Network | By extracting non-linear features and establishment of a non-linear model in this experiment, the newly established model is useful to predict the health status of older people. ANN is the best method in terms of acuuracy | Accuracy: Artificial Neural Network = 0.699, Logistic Regression = 0.672, Support Vector Machine = 0.672, XGBoost Classifier = 0.635, Random Forest Classifier = 0.606 |
| 4 | Engchuan et al. [19] | 2019 | To evaluate the determinants of health in ageing using machine learning methods and to compare the accuracy with traditional methods | 6,209 older adults from 6 waves of ELSA, ALTHOS project | Healthy Ageing/health status | ML models- Random Forest, Deep Learning (ANN), Linear Model |
Two models were created 1) A linear model was used to generate the new health metric variable by using sociodemographic variables 2) A health metric prediction model was built by fitting the health metric with the time from previous 4 waves of data. RF was the best performing model |
MSE (Mean Square Error): Random Forest = 51.11, Linear Model = 52.07, Deep Learning = 59.08, random prediction = 418.40 |
| 5 | Wong et al. [20] | 2021 | To identify and characterise different ageing pathways and associated ageing profiles using multivariate regression trees | 25,742 adults from 33 European countries from the fourth European Quality of Life Surveys (EQLS), 2016 | Successful Ageing | Unsupervised ML- Multivariate Regression Trees (MRT) | The study identified neighborhood characteristics that contributes to successful ageing and found that healthcare services affordability is a prominently relevant factor | NA |
| 6 | Huang et al. [21] | 2022 | To develop a variety of machine learning models based on psoas muscle tissue at the L3 level of unenhanced abdominal computed tomography to predict osteoporosis | 172 adults from hospital (2017 jan to 2021 jan). Collected the CT images and the clinical characteristics data of patients who underwent DXA and abdominal CT examination | Bone Disease |
Feature selection- Mann–Whitney U test, LASSO ML models-Gaussian Naïve Bayes (GNB), Random Forest(RF), Logistic Regression(LR), Support Vector Machines(SVM), Gradient Boosting Machine(GBM), XGBoost(XGB) |
Gradient Boosting Mahine had the best predictive performance | AUROC (LR = 0.85, XGB = 0.82, GNB = 0.8, GBM = 0.86, RF = 0.87, SVM = 0.81) Sensitivity (LR = 0.73, XGB = 0.7, GNB = 0.73, GBM = 0.7, RF = 0.73, SVM = 0.86), Specificity (LR = 0.86, XGB = 0.75, GNB = 0.86, GBM = 0.92, RF = 0.86, SVM = 0.55), Accuracy (LR = 0.8, XGB = 0.72, GNB = 0.8, GBM = 0.81, RF = 0.8, SVM = 0.71) |
| 7 | Birks et al. [22] | 2017 | To evaluate the risk algorithm derived in Israel on the Clinical Practice Research Datalink (UK) | 2550119 patient's primary care electronic health record data | Cancer |
Retrospective analysis Model was trained using Israel data and this study tests the model on UK data ML model—Random Forest |
The risk score applied to routinely collected primary care data from the UK produced AUC values comparable with those from the Israeli population used to derive it. Age is a crucial factor determining colorectal cancer risk, and the addition of Full Blood Count indices to age and sex improves the identification of patients at risk | NA |
| 8 | Sasani et al. [23] | 2019 | To utilize a machine learning approach to develop an algorithm based on components of the geriatric assessment, other than Timed Up and Go (TUG) test, to accurately predict which patients will have slower TUG times | Electronic medical record—eRFA (1901 patients) | Survival among Cancer Patients | Decision tree classifier | A simple decision tree was able to predict patient gait speed with high accuracy and can be used to screen patients who need further functional assessment or intervention | Accuracy = 78%, Specificity = 90%, Sensitivity = 66% of the prediciton |
| 9 | Bosch et al. [24] | 2021 |
1) To predict quality indicators for colorectal cancer surgery 2) To identify previously unrecognized predictors of 30 day mortality, based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities |
62,501 patient's data who underwent resection for primary colorectal cancer registred Dutch ColoRectal Audit | Survival among Cancer Patients |
Multivariable Logistic regression, Elasticnet regression, Support vector machine, Random forest, Gradient boosting Risk factors idebtifies by—Logistic regression, Shapley Additive Explanations (SHAP) values |
The LR analysis reveals some rare but high-impact comorbidities, such as pulmonary fibrosis, lung surgery or transplant, cardiac valve replacement, and liver failure SHAP analyses revealed that the ASA score and the specific comorbidities of COPD and asthma, hypertension, and myocardial infarction are important variables for predicting postoperative mortality |
NA |
| 10 | Tseng et al. [25] | 2023 |
1) to validate perviously created biomarkers created for cardiovascular disease (CVD) risk 2) enhance risk assessment in individuals |
48,260 subjects with no history of CVD collected clinical data and retinal photographs from UK Biobank | cardiovascular disease | Reti-CVD scores were calculated and stratified into 3 risk groups. Cox proportional hazard models were applied to evaluate the ability of Reti-CVD for predicting CVD | Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions | NA |
| 11 | Huang et al. [26] | 2022 | To investigate the additive value of four groups of risk factors, based on ease of information availability and regular clinical workflow using ensemble ML | 600 Southeast Asian individuals from SingHEART prospective longitudinal cohort study | cardiovascular disease | Ensemble ML for low risk- Naïve Bayes, RF, Support Vector Classifier for high risk- Generalised Linear Regression, Support Vector Regressor, Stochastic Gradient Descent Regressor | The study used novel data sources, i.e., wearable devices data for prediction of CVD risk. Compared the CVD risk score against Framingham Risk Scores and ML algorithm performed better in identifying low risk individuals. Self-reported physical activity, average daily heart rate, awake blood pressure variability and percentage time in diastolic hypertension were important contributors to CVD risk classification | NA |
| 12 | Sajid et al. [27] | 2021 | To develop alternative ML-based risk prediction models (RPMs) that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs | 46 subjects from Punjab Institute of Cardiology, Pakistan through case–control study | cardiovascular disease | ML models- Artificial Neural Networks(ANN), Linear Support Vector Machine(LSVM), Decision Tree (DT) | ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities | ANN (AUC = 0.87, accuracy = 81.09, sensitivity = 0.78, specificity = 0.84), Linear SVM (AUC = 0.86, accuracy = 80.86, sensitivity = 0.81, specificity = 0.81), RF (AUC = 0.86, accuracy = 78.30, sensitivity = 0.80, specificity = 0.76) |
| 13 | Kobayashi et al. [28] | 2022 | To identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes | 827 subjects from STANISLAS to train models 1,394 subjects from Malmö Preventive Project cohort for validation | cardiovascular disease | Cluster analysis-performed K-means clustering based on echocardiograohic data Decision Tree- to find predictive factors | The study identified 3 echocardiographic phenotypes that can be easily identified in clinical practice | NA |
| 14 | Barbieri et al. [29] | 2022 |
1) to develop novel deep learning models for predicting the risk of CVD event 2) to compare the performance of the deep learning models and traditional Cox proportional hazards models on the basis of the proportion of explained variance, calibration and discrimination |
Study population from New Zealand, 2012 | cardiovascular disease | Cox Proportional Hazard Model Deep Learning | The largest hazard ratios estimated by the deep learning models were for tobacco use in women and chronic obstructive pulmonary disease with acute lower respiratory infection in men. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance calibration and discrimination | NA |
| 15 | Sanchez-Martinez [30] | 2021 | To develop a risk prediction model for incident Major Adverse Cardiovascular Events (MACE) from subjects enrolled in a large clinical trial in initially healthy, elderly individuals and to validate the model in a large primary care dataset | ASPirin in Reducing Events in the Elderly (ASPREE) study (18548 participants) | cardiovascular disease | Cox proportional hazard regression models for risk 5 yr risk prediction | A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function | AUC = 64.16 |
| 16 | Li et al. [31] | 2019 | To develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency | National Storke Screening Data, China, 2017 | cardiovascular disease | ML models to classify stroke risk levels—Logistic Regression, Naïve Bayes, Bayesian Network Model, DT, Neural Networks, RF < Bagged DT, Voting and Boosting model with DTs | The model developed in this study has capacity to mprove the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures | LR (precision = 90.56, recall = 96.35, F1 score = 93.37, AUC = 97.96), NB (precision = 66.96, recall = 94.99, F1 score = 78.55, AUC = 96.64), BN (precision = 67.5 recall = 93.85, F1 score = 78.52, AUC = 96.86), DT (precision = 91.95, recall = 98.12, F1 score = 94.93, AUC = 99.36), NN (precision = 91.82, recall = 98.52, F1 score = 95.05, AUC = 99.23), RF (precision = 96.89, recall = 95.76, F1 score = 96.32, AUC = 99.41), Bagging DT(precision = 92.21, recall = 98.98, F1 score = 95.43, AUC = 99.39), Boosting DT (precision = 94.89, recall = 99.12, F1 score = 96.96, AUC = 99.41) |
| 17 | Moradifar et al. [32] | 2022 |
1) to identify socio-economic, life style, and metabolic factors associated with hyperglycemia 2) to compare the ability of 5 commonly used ML algorithms for prediction of hyperglycemia on a population based study |
17,705 individuals from Surveillance of Risk Factors of Noncommunicable Disease (STEPs study), Iran 2016 | Diabetes Mellitus | ML models-random forest, multivariate logistic regression, gradient boosting, support vector machines, and artificial neural network | Being older age, high BMI status, having hypertension, consuming fish more than twice per week, abdominal obesity were 5 most important risk factors for hyperglycemia. The study shows that survey based screening tools can be used for hyperglycemia prediction, without using blood test | RF (Accuracy = 0.70, specificity = 0.70, sensitivity = 0.68, AUC = 0.70, F1 score = 0.58), XGB (Accuracy = 0.69, specificity = 0.68, sensitivity = 0.72, AUC = 0.70, F1 score = 0.58), SVM (Accuracy = 0.70, specificity = 0.69, sensitivity = 0.70, AUC = 0.70, F1 score = 0.58), LR (Accuracy = 0.70, specificity = 0.70, sensitivity = 0.70, AUC = 0.70, F1 score = 0.58), ANN (Accuracy = 0.69, specificity = 0.69, sensitivity = 0.71, AUC = 0.70, F1 score = 0.58) |
| 18 | Chen et al. [33] | 2019 | To develop a machine learning pipeline to investigate the method’s discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity | 115 subjects from Cross section study &2-year time prospective longitudinal study | Diabetes Mellitus | Ml methods-Principal Component Analysis, feature selection, logistic regression classifier | The machine learning based method is superior to the widely-used univariate group comparison method. The individual perfusion diabetes pattern score is a highly promising perfusion imaging biomarker for tracing the disease progression of individual T2DM patients | NA |
| 19 | Mansoori et al. [34] | 2023 | To assess the potential association between T2DM and routinely measured hematological parameters | 9000 adults from Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study | Diabetes Mellitus | ML models-logistic regression, decision tree, Bootstrap Forest (BF) | BF model performed the best. The most effective factors in the BF model were age and WBC (white blood cell). The BF model represented a better performance to predict T2DM | NA |
| 20 | Lai et al. [35] | 2019 |
1) to build an effective predictive model using ML to identify the population at risk of diabetes mellitus based on lab information and demographic data of patients 2) to compare the predictability of ML models |
13,309 Canadian patients from Canadian Primary Care Sentinel Surveillance Network (CPCSSN) | Diabetes Mellitus | ML models -logistic regression, Gradient Boosting Machine (GBM), decision tree, random forest | The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. Fasting blood glucose, body mass index, highdensity lipoprotein, and triglycerides were the most important predictors in these models | AROC (GBM = 84.7%, Logistic Regression = 84.0%, Random Forest = 83.4%, RPART = 78.2%) |
| 21 | Li et al. [36] | 2021 | To use ML to select sleep and pulmonary measures associated with hypertension development | Prospective cohort study 860 individuals from Sleep Heart Health Study (SHHS), 261 developed hypertension after 5 years | Hypertension | Penalized Regression | A unique combination of sleep and pulmonary function measures (using ML) better predicts hypertension compared to the apnoea-hypopnea index | NA |
| 22 | Zhong et al. [37] | 2023 | To develop a superior ML model based on easily collected variables to predict the risk of early cognitive impairement in hypertension individuals | Multicenter observational study including 733 hospitalized hypertensive patients | Hypertension |
Feature Selection -LASSO regression ML classifiers—Logistic Regression, XGBoost, Gausian Naïve Bayes |
Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension XGBoost performed best |
LR (AUC = 0.83, accuracy = 0.74, sensitivity = 0.78, specificity = 0.73, F1 score = 0.50), XGB (AUC = 0.88, accuracy = 0.81, sensitivity = 0.84, specificity = 0.80, F1 score = 0.59), GNB (AUC = 0.74, accuracy = 0.75, sensitivity = 0.74, specificity = 0.74, F1 score = 0.50) |
| 23 | Sun et al. [38] | 2022 | To investigate the association between waist circumference and the development of hypertension | 5,330 individuals from CHARLS | Hypertension |
Adjusted Cox Regression Model and visualized by restricted cubic splines, sensitivity analysis of cox regression in different subgroups ML models—Random Forest, XGBoost |
The random forest method and the Extreme Gradient Boosting method revealed waist circumference as an important feature to predict the development of hypertension. The sensitivity analysis indicated a consistent trend between waist circumference and new‐onset hypertension in all BMI categories | NA |
| 24 | Alkaabi et al. [39] | 2020 | To construct and compare predictive models to identify individuals at high risk of developing hypertension without the need of invasive clinical procedures | 987 individuals from Qatar Biobank study | Hypertension | ML models-Decision Tree, Random Forest, Logistic Regression | In terms of AUC, compared to logistic regression, random forest and decision tree had a significantly lower discrimination ability. Age, gender, education level, employment,tobacco use, physical activity, adequate consumption offruits and vegetables, abdominal obesity, history ofdiabetes, history ofhigh cholesterol, and mother’s history high blood pressure were important predictors ofhypertension | LR (accuracy = 81.1%, PPV = 80.1%, sensitivity = 81.1%, F measure = 80.3%), DT (accuracy = 82.1%, PPV = 81.2%, sensitivity = 82.1%, F measure = 81.4%), RF (accuracy = 82.1%, PPV = 81.4%, sensitivity = 82.1%, F measure = 81.6%) |
| 25 | Alghafees et al. [40] | 2022 | To use machine learning to predict the stone free stauts after percutaneous nephrolithotomy (PCNL) among patients | 137 patients who ahave undergone PCNL at a hospital of Soudi Arabia. A 12 month followup study was done between 2020–2022 | Kidney Disease | Supervised ML-Logistic regression, Random forest, XGBoost regressor | A inverse relation was found between Stone Free Status and Chronic Kidney disease and Hypertension. Random forest model showed the highest efficacy in predictingstone free status | Accuracy: LR = 71.4%, XG Boost = 74.5%, RF = 75% |
| 26 | Jadlowiec et al. [41] | 2022 | To better understand differeing delayed graft function (DGF) outcomes, clustering approach was used to categorize clinical phenotypes of Kidney Transplant recepients with DGF and their paired donors | 17,073 patients who received kidney transplant in the USA (2015–19) were identified using Organ Procurement and Transplantation/ United network for Organ Sharing database | Kidney Disease |
Estimation of cumulative risks of death censored graft failure and death after kidney transplant—Kaplan–Meier analysis Comparision of risk among assigned clusters—Cox proportional hazaed analysis Clustering (ML)—Unsupervised consensus clustering approach |
By applying a ML clustring approach, the current study has allowed for an unbiased assessment of kidney transplant outcomes for those with DGF 4 clusters were characterized: cluster 1- younger, low BMI, non-diabetic, kidney re-transplant recipients who had a high PRA (panel reactive antibody). cluster2—oldest of the four clusters, had a higher BMI, were likely to have lower functional status, and be diabetic with 3 + years of dialysis vintage. They were also the most likely to receive ECD (extended croterion donor) high KDPI (kidney donor profile index) kidneys. cluster 3—young and non-diabetic. They were more likely to be black, have hypertension and receive higher HLA mismatched, lower KDPI kidneys. cluster 4—middle-aged, firsttime KT recipients with either diabetes or hypertension, lower functional status, dialysis duration ≥ 3 years, and a low PRA |
NA |
| 27 | Sabanayagam et al. [42] | 2020 | To detect chronic kidney disease from retinal images using deep learning algorithm (DLA) |
3 population based, multiethnic, cross-sectional studies from Singapore and China. For deleopment and validation of DLA—Singapore epidemiology of eye diseases study(SEED) For ecternal testing—Singapore prospective study program (SP2), Beijing eye study (BES) |
Kidney Disease |
3 models were trained- 1) image DLA 2) risk factors (RF) including age, sex, ethnicity, diabetes and hypertension 3) hybrid DLA combining image and RF |
The image-only DLA and clinical RF models both achieved high AUCs in SEED internal validation. The performance of image DLA in subgroups of patients with diabetes and hypertension was similar to that in the whole group. Thus, for chronic kidney disease detection, a retinal image-only DLA is similar to information from a classic RF model, and supports the potential of using retinal photography to detect chronic kidney disease in specific settings | AUC, sensitivity, specificity, PPV, NPV were reported for image only, RF, only and hybrid differently for the datasets |
| 28 | Cho et al. [43] | 2021 | To develop a model for predicting suicide among elderly popoulation by using ML | 48,047 South Korean elderly data from National Health Insurance Sharing Service (NHISS) | Mental Health | ML model—Random Forest | The study developed a model for predicting suicide that occurs infrequently using ML. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group | Random Forest (AUC = 0.818, accuracy = 0.832, sensitivity = 0.600, specificity = 0.833, NPV = 0.999, PPV = 0.007) |
| 29 | Kasthurirathne et al. [44] | 2019 | To build decision models caapble of predicting the need of advanced care for depression across patients | 84,317 individuals from Primary Care Visit at Eskenazi Health, Indiana | Mental Health | ML model—Random forest decision models | This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history | AUC, optimal sensitivity, optimal specificity was reported for different patient groups |
| 30 | Zhang et al. [45] | 2022 | To explore the spatial patterns of urban streetscape features and their associations with residents’ mental health by age and sex in Zhanjiang, China |
Study area where images are captured-Zhanjiang City, China Mental health data—813 patients suffering psychiatric disorders from hospitalization data of Guangdong Medial University |
Mental Health |
Image capturing- Baidu Street View physical features- Green View Index (GVI) Spatial distributions- Global Moran's I and hotspot analysis Deep learning methods—Fully Convolutional Network for semantic image segmentation |
The Results of Pearson’s correlation analysis show that residents’ mental health does not correlate with GVI, but it has a significant positive correlation with the street enclosure, especially for men aged 31 to 70 and women over 70-year-old | NA |
| 31 | Opoku et al. [46] | 2021 |
1) to investiagate the feasibility of predicting depression with human behaviours quantified from smart phone datasets 2) to identify behaviours that can influence depression |
Data of 629 participants collected in a longitudinal observational study with the Carat app in 6 months interval Smart phone datsets and self-reported 8-item Patient Health Questionnaire depression assessments | Mental Health | the relationship between the behavioral features and depression—correlation and bivariate linear mixed models (LMMs) ML models- RF, SVM with radial basis function kernel, XGBoost, KNN, Logistic Regression | Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors’ data. Screen and internet connectivity features were the most influential in predicting depression | RF (accuracy = 97.97, precision = 92.50, recall = 94.38, F1 = 93.41, AUC = 98.83, cohen kappa = 92.21), XGB (accuracy = 98.14, precision = 92.51, recall = 95.56, F1 = 94.0, AUC = 99.06, cohen kappa = 92.90), SVM (accuracy = 85.68, precision = 51.98, recall = 80.67, F1 = 63.20, AUC = 89.47, cohen kappa = 54.83), LR (accuracy = 59.27, precision = 20.29, recall = 57.25, F1 = 29.95, AUC = 62.43, cohen kappa = 9.66), KNN (accuracy = 96.44, precision = 85.55, recall = 92.19, F1 = 88.73, AUC = 94.69, cohen kappa = 86.61) |
| 32 | Ewbank et al. [47] | 2020 |
1) to generate a quantifiable measure of psychotherapy treatment 2) to determine the association between the quantity of each aspect of therapy delivered and clinical outcomes |
17,572 patients receiving cognitive behavioural therapy (CBT) collected through internet | Mental Health | Multivariable Logistic Regression, for classification—Deep Learning | The finding supports the principle that CBT change methods help produce improvements in patients presenting symptomps. Deep leaning allows us to extract this knowledge to provide valuable insights into therapy that were previously unavailable to an individual therapist | NA |
| 33 | Guntuku et al. [48] | 2019 |
1) to characterise the lives of people who mention the wordd 'lonely' and 'alone' in their Twitter timeline 2) to correlate their posts with predictors of mental health |
6202 Twitter users who used 'lonely' and 'alone' in their posts in the timeline 2012 to 2016 Control group matched by age, gender and period of acitivity |
Mental Health |
Language features extracted by- Open vocabulary, Dictionary based, Mental well-being attributes, Use of drug words, Temporal patterns For predicitng the likelihood of posting (ML model)—Random Forest |
The cases of loneliness attributed to difficult interpersonal relationships, psychosomatic symptopms, substance use, wanting change, unhealthy eating and having troubles with sleep. These posts were also aqssociated with linguistic markers of anger, depression and anxiety The Random Forest model predicted expression of lonliness with around 86% accuracy score |
AUC, F1 score, accuracy, precision, recall values are given for different features |
| 34 | Helbich et al. [49] | 2019 |
1) to compare streetscape metrics derived from street view images with statellite-derived ones for the assessment of green and blue space 2) to examine associations between exposure to green and blue spaces as well as geriatric depression |
Questionnaire based cross-sectional study of 1190 individuals Image data- streetview images and satellite images |
Mental Health | Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were analyzed using a fully convolutional Neural Network Depressive symptoms were assessed through the shortened Geriatric Depression Scale (GDS-15) and analyzed by Multilevel Regressions |
Metrics of green and blue space derived from street view images were not correlated with satellite-based ones Multilevel regressions showed that both street view green and blue spaces were inversely associated with GDS15 scores No significant associations were found with NDVI, NDWI, and GlobeLand30 green and blue space |
NA |
| 35 | Kim et al. [50] | 2021 |
1) To classify and predict associations between nutritional intake and risk of overweight/ obesity, dyslipidemia, hyeprtension and type 2 diabetes mellitus (T2DM) 2) To develop a deep neural network (DNN) model and compare it with the machine learning models |
4th to 7th Korea National Health and Nutrition Examination Survey (KNHANES) samples: dylipidemia = 10,731 hypertension = 10,991 T2DM = 3,889 overweight/obesity = 10,980 | Multimorbidity | DNN (binary cross entropy loss funtion for binary classification) Stuctural Equation Modeling performed to simultaneously estimate multivariate causal association between nutritional intake and the specified diseases. ML models for comparision—logistic regression, decision tree | DNN has better prediciton accuarcy than 2 conventional machine learning models. Energy intake was the most influential factor in risk of dyslipidemia, hypertension and overweight/obesity. (here, Nutritional intake includes food intake, energy intake, protein intake, fat intake, carbohydrate intake, sodium intake and potassium intake) | Accuracy according to diseases: Dyslipidemia (DNN = 0.58654, LR = 0.58448, DT = 0.52148), Hypertension (DNN = 0.79958, LR = 0.79929, DT = 0.66773), T2DM (DNN = 0.80896, LR = 0.80818, DT = 0.71587), Overweight/obesity (DNN = 0.62496, LR = 0.62486, DT = 0.54026) |
| 36 | Sone et al. [51] | 2022 | To investigate the relationships between brain aging and relevant mental factors as well as lifestyle-related metabolic diseases in a cognitively unimpaired population of older participants | community-based cohort study (773 participants) | Multimorbidity | Multiple regression analysis | The analysis identified life satisfaction, diabetes, and use of alcohol as significantly independent predictors for brain age in a community-based elderly cohort. Resilience may also be important. It is possible that people could keep their brains younger by improving their subjective life satisfaction, avoiding alcohol use disorder, and preventing the development of diabetes | NA |
| 37 | Byeon et al. [52] | 2021 | To examine Alzheimer's patients living in South Korea to understand the predictors of anxiety using boosting algorithms and data-level approach and compare the performance | Rehabilitation hospitals for early dementia screening (253 individuals) | Multimorbidity |
Boosting algorithms (AdaBoost and XGBoost) Data-level approach (raw data, undersampling, oversampling, and SMOTE) |
Using a SMOTE-XGBoost model may provide higher accuracy than using a SMOTE-Adaboost model for developing a prediction model using outcome variable imbalanced data such as disease data in the future | XGBoost based on SMOTE (accuracy = 0.84, sensitivity = 0.85, and specificity = 0.81) |
| 38 | Mahajan et al. [53] | 2021 | To develop a physiologically diverse and generalizable set of multimorbidity risk score | 9,92,868 patient's records from a 2 year followup electronic health record | Multimorbidity |
Built 6 ML models for scoring risk of heart, lung, neuro, kidney, digestive functions and a combined score for all ML models- Logistic regression, gradient boosted tree classifier |
The total health score (THS) created in this study, outperformed other scores created by using conventional methods (Charlson comorbidity index and Elixhauser comorbidity index). The performance of the newly created score was most accurate for middle aged, lower-income subgroups |
Total health score (AUROC = 0.823, sensitivity = 0.721, specificity = 0.777) Also, AUROC < sensitivity, specificity scores are given for all the diseases |
| 39 | Spooner et al. [54] | 2020 | To develop ML models that predict survival to dementia using baseline data from different studies | Longitudinal ageing studies- Sydney Memory and Ageing Study (MAS), Alzheimer’s Disease Neuroimaging Initiative (ADNI) | Neurodegenrative Disease | 8 feature selectio n methods: Filter methods- univariate cox score, RF variable importance, RF minimal depth, RF variable hunting, RF maximally selected rank statistics,mRMR Wrapper methods- sequential forward selection, sequential forward floating selection. ML models- LASSO, Ridge, ElsticNet regression, CoxBoost, GLMBoost, XGBoost, random survival forest, maximally selected rank statistics from random surival forest. evaluation metric- c index |
Ridge regression outperformed LASSO in all personalised Cox regression methods Among the boosted models, CoxBoost was the best performer. The maximally selected rank statistics RF outperformed the other random forest models. In case of feature selection, RF min depth filter produced most accurate models |
NA |
| 40 | Tan et al. [55] | 2023 | To develop a reliable ML model using socio-demographics, vascular risk factors, and structural neuroimaging markers for early diagnosis of cognitive impairement in multi-ethnic Asian population | 911 participants from Epidemiology of Dementia in Singapore study | Neurodegenrative Disease | ML models- logistic regression, support vector machine, gradient boosting machine voting ensemble- SHAP | According to the voting ensemble, the important predictors of cognitive impairement are age, ethnicity, education attainment, and structural neuroimaging | LR (accuracy = 0.71, F1 = 0.78, AUC = 0.69, FPR = 0.38, sensitivity = 0.75, specificity = 0.62, PPV = 0.81, NPV = 0.54), SVM (accuracy = 0.74, F1 = 0.81, AUC = 0.71, FPR = 0.40, sensitivity = 0.81, specificity = 0.60, PPV = 0.81, NPV = 0.59), GBM (accuracy = 0.73, F1 = 0.79, AUC = 0.73, FPR = 0.29, sensitivity = 0.74, specificity = 0.71, PPV = 0.85, NPV = 0.56), Ensemble (accuracy = 0.83, F1 = 0.87, AUC = 0.80, FPR = 0.26, sensitivity = 0.86, specificity = 0.74, PPV = 0.88, NPV = 0.72) |
| 41 | Hu et al. [56] | 2021 | To build a prediction model based on ML for cognitive impairement among Chinese community dwelling elderly people with normal cognition | 6718 individuals of age > 60, with MMSE score > = 18, not having any severe disease from the Chinese Longitudinal Health Longevity Survey (CLHLS) | Neurodegenrative Disease |
To access 3-year risk of developing cognitive impairement, Ml models used- Random forest, XGBoost, Naïve Bayes, Logistic regression A nomogram was established to vividly present the prediction model |
Features selected to develop model- age, instrumental activities of daily living, marital status, and baseline cognitive function Older people with nomogram score less than 170 are considered to have a low 3-year risk, and more than 173 are considered at higher risk |
AUC, optimal cut off, sensitivity, specificity, accuracy, specificity/sensitivity values were reported for logisitc regression, random forest, naïve bayes, XG Boost both for validation dataset and test dataset |
| 42 | Fukunishi et al. [57] | 2020 | To predict the risk of Alzheimer-type dementia for persons aged over 78 without receiving long-term care services using regularly collected claim data | 48,123 persons from claim data of health insurance and long-term care insurance in Japan | Neurodegenrative Disease | ML models- Sparse logistic regression models with L0, L1 regularization | SLR-L0 is more effective than SLR-L1 in dealing with a large number of features and useful for practical use. It can be extended to prediction of various diseases | SLR-L0 (Accuracy = 0.639, Precision = 0.105, Recall (Sensitivity) = 0.617, Specificity = 0.641, False positive rate = 0.359, False negative rate = 0.383, F-measure = 0.180, AUC = 0.663, Average precision = 0.124), SLR-L1 (Accuracy = 0.623, Precision = 0.103, Recall (Sensitivity) = 0.633, Specificity = 0.622, False positive rate = 0.378, False negative rate = 0.367, F-measure = 0.177, AUC = 0.660, Average precision = 0.122) |
| 43 | Shi et al. [58] | 2021 | To analyze the relationship between ageing, cellular homeostasis and Neurodegenrative diseases, as well as the relative mechanism involced | DNA methylation profiles obtained from Gene Expression Omnibus (GEO) database | Neurodegenrative Disease |
Feature selection- ReliefF Supervised ML |
The extracellular fluid, cellular metabolisms, and inflammatory response were identified as the common driving factors of cellular homeostasis imbalances during the accelerated aging process | NA |
| 44 | Lian et al. [59] | 2020 | To identify and classify Alzheimer's disease using a novel Weakly supervised learning based deep learning (WSL- based DL) framework | 2 brain MRI datasets 2D MRIand #D MRI data) one from Kaggle | Neurodegenrative Disease | WSL-based deep learning (DL) framework (ADGNET) | The ADGNET has higer F-score and sensitivity value, outperforming two state of art models (ResNext WSL and SimCLR) | Kappa score, sensitivity, specificity, precision, accuracy, F1 score values are reported for all the models and datasets |
| 45 | Szlejf et al. [60] | 2023 | To develop and test ML models to predict cognitive impairement using variables obtainable in primary care settings, | 8,291 participants from a cross-sectional study ELSA-Brasil | Neurodegenrative Disease | ML models- Logistic regression, neural networks, gradient boosted trees | XGBoost presented the highest discrimination in predicting cognitive impairement than the other models. Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm | XGBoost 0.873, ROC-AUC = 0.316, sensitivity = 0.969, specificity = 0.298, PPV = 0.972, NPV = 0.307 76.53% LightGBM 0.860 (0.821–0.898) 0.398 0.967 0.331 0.975 0.361 72.44% Logistic Regression 0.805 (0.762–0.847) 0.235 0.964 0.209 0.969 0.221 61.22% ANN 0.801 (0.755–0.845) 0.204 0.967 0.200 0.967 0.202 66.32% Catboost 0.805 (0.762–0.847) 0.102 0.989 0.270 0.964 0.148 61.22% |
| 46 | Benhamou et al. [61] | 2021 |
Hypothesis 1- Frontotemporal dementia syndromes would be associated with more severe impairments of musical deviant detection and autonomic reactivity than would Alzheimer’s disease. Hypothesis 2- Sensitivity to information-theoretic parameters of melodies (deviant surprise, melody entropy) would be relatively more severely reduced in bvFTD and svPPA than in other participant groups Hypothesis 3- Cognitive coding of musical surprise in the patient cohort would have separable neuroanatomical correlates within the hierarchical distributed brain networks previously implicated in processing different kinds of musical information |
case- 62 patients with frontotemporal dementia, typical amnestic Alzehimer's disease control- 33 healthy persons | Neurodegenrative Disease |
Regression model for that took elementary perceptual, executive and musical competence into account, assessed accuracy detecting melodic deviants And pupillary responses and related these to deviant surprise value and carrier melody predictability, calculated using an unsupervised ML model of music |
Major dementias have distinct profiles of sensory ‘surprise’ processing, as instantiated in music Music may be a useful and informative paradigm for probing the predictive decoding of complex sensory environments in neurodegenerative proteinopathies, with implications for understanding and measuring the core pathophysiology of these diseases |
XG Boost (AUC-ROC = 0.87, sensitivity = 0.31, specificity = 0.96, PPV = 0.29, NPV = 0.97, F1 score = 0.30), LightGBM (AUC-ROC = 0.86, sensitivity = 0.39, specificity = 0.960.96, PPV = 0.33, NPV = 0.97, F1 score = 0.36), LR (AUC-ROC = 0.80, sensitivity = 0.23, specificity = 0.96, PPV = 0.20, NPV = 0.96, F1 score = 0.22), ANN (AUC-ROC = 0.80, sensitivity = 0.20, specificity = 0.96, PPV = 0.20, NPV = 0.96, F1 score = .20), Catboost (AUC-ROC = 0.80, sensitivity = 0.10, specificity = 0.98, PPV = 0.27, NPV = 0.96, F1 score = 0.14) |
| 47 | Ithapu et al. [62] | 2014 | To detect and quantify White Matter Hyperintensities (WMH) observed in T2 FLAR images of subjects with risk of neurological disorders, especially Alzheimer's disease | T1 and T2-MRI scans of 251 individuals from Wisconsin Alzheimer's Disease Research Center | Neurodegenrative Disease | ML models- Random forests, Support Vector machines | Random Forest based regression works the best with significant improvement over the current state-of-the-art unsupervised model | SVM (F = 0.54, precision = 0.56), RF (F = 0.67, precision = 0.79) |
| 48 | Gharbi-Meliani et al. [63] | 2023 |
1) to built a clustering analysis for identifying transition to high likelihood dementia in population ageing surveys 2) to demostrate that the suggested model can identify probable dementia in surveys where dementia is wither poorly or non-diagnosed, and that the method is also efficient to study the risk factors |
For model building- wave 1 & 2 of Survey of Health, Ageing, and Retirement in Europe (SHARE) validation set- English Longitudinal Study of Ageing (ELSA) waves 1–9 Harmonised datasets from the Gateway to Global Aging |
Neurodegenrative Disease | The discrimination power of the proposed clustering algorithm was evaluated by counting on its identification of "likely dementia" status compared with the self-reported dementia status Unsupervised ML—Multiple Factor Analysis (MFA) followed by Hierarchical Clustering on Principal Components (HCPC) | “Likely Dementia” status was more prevalent in older people, displayed a 2:1 female/male ratio and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy | NA |
| 49 | Ford et al. [64] | 2019 | To detect existing or developing dementia on patients which is currently undetected as having the condition by the general practice | case–control design 93,120 patients from electronic patient records from Clinical Practice Research Datalink (CPRD) | Neurodegenrative Disease | ML classifiers to discriminate between cases and controls—logistic regression with lasso, naïve bayes, support vector machines, random forest, neural network |
The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Naïve Bayes model performed least well Logistic regression and random forest algorithms may nevertheless offer an advantage over support vector machines and neural networks as they produce easy to interpret |
Logistic Regression with Lasso (AU-ROC = 0.736, specificity = 0.752, sensitivity = 0.602, PPV = 0.156), Naïve Bayes (AU-ROC = 0.682, specificity = 0.906, sensitivity = 0.241, PPV = 0.164), SVM (AU-ROC = 0.737, specificity = 0.691, sensitivity = 0.674, PPV = 0.142), RF (AU-ROC = 0.734, specificity = 0.653, sensitivity = 0.700, PPV = 0.134), NN (AU-ROC = 0.737, specificity = 0.781, sensitivity = 0.619, PPV = 0.178) |
| 50 | Casanova et al. [65] | 2020 | To evaluate modifiable and genetic risk factors for Alzheimer's disease to predict cognitive decline | 7,142 paricipants with DNA and > 2 cognitive evaluations from HRS (Health and Retirement Study) | Neurodegenrative Disease | To determine the form and number of classes- Latent class trajectory analysis ML model- Random forest classifier |
Top ranked predictors were education, age, gender, stroke, NSES, and diabetes, APOE ε4 carrier status, and BMI RF classification techniques suggested that nongenetic factors contribute more to cognitive decline than genetic factors. Education was the most relevant predictor for discrimination |
NA |
| 51 | Aguayo et al. [66] | 2023 | To compare the performance of different types of deep neural networks (DNNs) with regularized Cox proportional hazard models to predict neurodegenrative diseases in older population | 5433 participants having no neurodegenerative condition from wave 2 & wave 8 for followup from ELSA study | Neurodegenrative Disease | Outcome- new events of Parkinson's, Alzheimer's or Dementia. Models- DNNs- Feedforward, TabTransformer, Densenet Cox models—CoxEn, CoxSf | TabTransformer is promising for prediction of neurodegenrative diseases with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for neurodegenrative diseases | NA |
| 52 | Oscar et al. [67] | 2017 | To develop and demonstrate a supervised method for coding the content of sample of tweets on several dimensions relevant to Alzheimer's disease stigma on social media platform (twitter) | 31,150 tweets related to Alzheimer's disease (AD) collected through Twitter's search API 9 AD related keywords were searched | Neurodegenrative Disease |
Tweets were coded into 6 dimensions- informative, joke, metaphorical, organization, personal experience, ridicule. Classifier- N-grams Content analysis- Linguistic Inquiry and Word Count (LIWC) |
The study identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase “excess disability” | NA |
| 53 | König et al. [68] | 2021 | To investigate the association between automatically extracted speech features and neuropsychiatric symptomps (NPS) in patients with mild NPS | 141 patients with NPS. From hospital records | Neurodegenrative Disease | A clinical score NPI (neuropsychiatric inventory) was used for the assessment. ML models- Support vector regression, Lasso linear regression | Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores. Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks | NA |
| 54 | Prange and Sonntag [69] | 2022 | To use digital pen features, such as geometrical, spacial, temporal and pressure characteristics to model user's cognitive performance (binary classification) | 40 subjects from a geriatric daycare clinic | Neurodegenrative Disease |
Traditional approach—content analysis of drawn features Current approach- digital cognitive assessment ML models- SVM, LR, nearest neighbors, naïve bayes, DT, RF, AdaBoost, Gradient boosted trees, deep learning |
ML techniques our feature set outperforms all previous approaches on the cognitive tests considered, i.e., the Clock Drawing Test, the Rey-Osterrieth Complex Figure Test, and the Trail Making Test in a binary classification task | Accuracy, F1 score, Log loss, Precision, Recall, AUC was calculated for feature subsets |
| 55 | Younan et al. [70] | 2020 | 1) to examine whether PM2.5 (particulate matter) affects the episodic memory decline, 2) to explore the potential mediating role of increased neuroanatomic risk of Alzheimer’s disease associated with exposure | 531 older females from Women's Health Initiative Study of Cognitive Ageing & the Women's Health Initiative Memory Study of Magnetic Resonance Imaging (1999–2010) | Neurodegenrative Disease | Subjects were assigned Alzheimer's disease pattern similarity scores through brain MRI. Method applied- Structural Equation Modeling (SEM) | The continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer’s disease risk, independent of cerebrovascular damage | NA |
| 56 | Aschwanden et al. [71] | 2020 | To estimate the relative importance of selected predictors in forecasting cognitive impairement and dementia in a large scale population representative sample | 9,979 older adults from HRS | Neurodegenrative Disease |
Combined methodology Estimatinf relative importance- RF and survival analysis estimate effect size for imp vars- Cox proportional hazard model |
African Americans and individuals who scored high on emotional distress were at relatively highest risk for developing cognitive impairment and dementia. Lower education, Hispanic ethnicity, worse subjective health, increasing BMI were comparatively strong predictors for cognitive impairment | NA |
| 57 | Noh et al. [72] | 2021 | To use machine learning (ML) to identify important features of gait and physical fitness to predict a decline in global cognitive function in older adults | 306 older adults from a survey conducted in Busan, South Korea | Neurodegenrative Disease |
Feature ranking- simple linear regression, XGBoost ML models- SVM, DT, RF, Neural Network, LASSO, ElasticNet, SCAD, MCP |
Five optimal features were selected using elastic net on the LP data for men, and twenty optimal features were selected using support vector machine on the XI data for women. Thus, the important features for predicting a potential decline in global cognitive function in older adults were successfully identified | NA |
| 58 | Jia et al. [73] | 2020 | To identify variables associated with subsequent incident dementia using ML | 1,439 individuals from Monongahela-Youghiogheny Healthy Aging Team (MYHAT) cohort study (2006–08) | Neurodegenrative Disease | ML method- Markov modeling with Hybrid density-and partition-based (HyDaP) clustering | The probability of incident dementia was associated with worse self-rated health, more prescription drugs, subjective memory complaints, heart disease, cardiac arrhythmia, thyroid disease, arthritis, reported hypertension, higher systolic and diastolic blood pressure, and hearing impairment, depressive symptoms, not currently smoking, and lacking confidantes | NA |
| 59 | Garcia et al. [74] | 2019 | To investigate whether early behavioural signs of AD may be detected through dialogue interaction | Middle aged participants from PREVENT Dementia Study, 2015 | Neurodegenrative Disease | Proposed methods-Speech processing ML methods- linear generative classifiers, state-of-art deep architectures | The study introduced a novel approach to monitoring early signs of dementia through the analysis of spoken dialogue. Also, focused more on narrative speech (monologue), both from transcribed recordings and from signal processing of voice samples | NA |
| 60 | Liu et al. [75] | 2023 |
1) to explore the predictive value of machine learning in cognitive impairement, 2) to identify important factors for cognitive impairement |
2,326 older adults from baseline, year2, year4 followups from CHARLS (2011–2015) data | Neurodegenrative Disease | ML models for predicting cognitive impairement- Random Forest, Logistic Regression | Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4. BMI, Blood pressure, cholesterol, functioning functional limitations, age, and depression were identified as important predictors of cognitive impairment | Precision, recall, F score, accuracy of different models of RF anf LR are reported for cognitive impairement prediciton for different time period |
| 61 | Elgammal et al. [76] | 2022 | To propose a novel computational method to automatically classify various stages of Alzheimer’s Disease based on the utilization of multifractal geometry analysis |
Kaggle (560 MRI) Alzheimer's Disease Neuroimaging Initiative (ADNI) database (750 MRI images) |
Neurodegenrative Disease |
Multifractal analysis K-nearest neighbour algorithm, XG Boost, Random Forest |
The proposed technique has achieved 99.4% accuracy and 100% sensitivity and the comparative results show that the proposed classification technique outperforms is recent techniques | XG Boost (accuracy = 73.2%, F1 score = 21.4%, ROC-AUC = 53.0%), RF (accuracy = 82.7%, F1 score = 83.30%, ROC-AUC = 82.70%) |
| 62 | Ghazal et al. [30] | 2021 | To classify multiple Alzheimer's disease stages using a novel methodology i.e. transfer learning | Kaggle repository (6393 image samples) | Neurodegenrative Disease | Transfer learning (Alexnet, a deep learning based network) | The proposed algorithm used the pertained AlexNet for the problem, retrained the CNN, and validated on validation dataset which gave an accuracy of 91.7% for multi-class problems on 40 epochs and the proposed system model does not require any hand-crafted features and it is fast or can easily handle small image datasets | The proposed model has accuracy of 73.75% |
| 63 | Sountharrajan et al. [77] | 2022 | To classify the patient records with dementia and non-dementia using different machine learning techniques from MRI brain images | Open Access Series of Imaging Studies (OASIS-2) dataset (150 patients) | Neurodegenrative Disease | logistic regression, Decision Tree (DT) classification, Random Forest (RF) classification, Support Vector Machine (SVM) classification and AdaBoost Classification | Random Forest (RF) classifier yields maximum accuracy, recall and AUC values. The hyperparameter tuning and Boruta algorithm added significance to the SVM and RF classification, thereby resulting in a F-score of 91% and 92% respectively | Logistic Regression-w/ imputation (accuracy = 78.95, recall = 70.00,AUC = 79.16), Logistic Regression-w/ dropna (accuracy = 75.00, recall = 70.00, AUC = 70.00), SVM (accuracy = 81.58, recall = 70.00, AUC = 82.22), Decision Tree (accuracy = 81.58, recall = 65.00, AUC = 82.50) Random Forest (accuracy = 84.21, recall = 80.00, AUC = 84.44), AdaBoost (accuracy = 84.21, recall = 65.00, AUC = 84.50) |
| 64 | Toshkhujaev et al. [78] | 2020 | To classify Alzheimer's disease using T1-weighted structural MRI | National Research Center for Dementia homepage, Alzheimer’s Disease Neuroimaging Initiative (ADNI), Alzheimer’s Disease Repository Without Borders, National Alzheimer’s Coordinating Center (701 paricipants) | Neurodegenrative Disease | Principal component analysis, support vector machine radial basis function classifier | A novel method for automatic classification, Alzheimer’s Disease from mild cognitive impairment and an health control was developed with more than 80% accuray for every dataset considered in the study | NA |
| 65 | Li and Yang [79] | 2021 | To build machine learning-based MRI data classifiers to predict Alzheimer's disease and infer the brain regions that contribute to disease development and progression and systematically compared the three distinct classifier | T1-weighted MR images from Alzheimers Disease Neuroimaging Initiative (ADNI) (560 participants) | Neurodegenrative Disease | Support Vector Machine, 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet) to improve the performance of classifiers—transfer learning strategy | The comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the VGGNet and support vector machine approaches | SVM (accuracy = 0.90, sensitivity = 0.939, specificity = 0.851, AUC = 0.97), 3D-VGGNet (accuracy = 0.95, sensitivity = 0.914, specificity = 1, AUC = 0.994), 3D-ResNet (accuracy = 0.95, sensitivity = 0.943, specificity = 0.96, AUC = 0.995) |
| 66 | Romero-Rosales et al. [80] | 2020 | The main objective of this research isto improve classification accuracy and extend the set of possible genetic risk factors for Alzheimer's disease | National Institute on Aging—Late-Onset Alzheimer’s Disease Family Study: Genome-Wide Association Study for Susceptibility Loci (phs000168.v2.p2) NCBI & Genotypes and Phenotypes database (dbGaP) (5,220 individuals) | Neurodegenrative Disease | Bootstrapping Stage-Wise Model Selection (BSWiMS), LASSO, GALGO | The addition ofmarkers from an initial model plus the markers ofthe model fitted tomisclassified samples improves the area under the receiving operative curve by around 5%, reaching ~ 0.84, which ishighly competitive using only genetic information | BSWiMS (accuracy = 0.686, sensitivity = 0.626, specificity = 0.734, AUC = 0.680), GALGO (accuracy = 0.720, sensitivity = 0.616, specificity = 0.800, AUC = 0.708), LASSO (accuracy = 0.766, sensitivity = 0.663, specificity = 0.825, AUC = 0.744) |
| 67 | Wang et al. [81] | 2022 | To identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life | Hospital record (8407 patients) | Hospitalization outcome among dementia patients | Ensemble tree based model, logistic regression, decision tree, random forest, multilayer perceptron neural network | Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi-dementia groups | AUC-ROC for tenfold cross validation is reported for models |
| 68 | Tsang et al. [82] | 2020 | To build a novel ML approach to predict hospitalization of dementia patients and to identify individual features | Electronic health records (59,298 patients) | Hospitalization outcome among dementia patients | Deep neural networks—entropy regularization with ensemble deep neural networks (ECNN), Random Forest | The discovery and heuristic evidence of correlation provide evidence for further clinical study of said medical events as potential novel indicators. There also remains great potential for adaption of ECNN within other medical big data domains as a data mining tool for novel risk factor identification | ECNN (TPR = 0.746, TNR = 0.7662, PPV = 0.766, NPV = 0.744, accuracy = 0.755), RF (TPR = 0.746, TNR = 0.714, PPV = 0.710, NPV = 0.750, accuracy = 0.729) |
| 69 | Revathi et al. [83] | 2022 | (i) Predicting people with possibilities of Alzheimer in their late life by doing careful analysis on various risk factors associated with Alzheimer’s. (ii) Conducting a neuropsychological test called Cognitive Ability Test (CAT) to assess the cognitive decline of a person | Clinical data (2361 patients) | Neurodegenrative Disease | Support vector machine, random forest, multinomial logistic regression | The study classified the risk factor using the operational definitions: “No Alzheimer’s,” “Uncertain Alzheimer’s,” and “Definite Alzheimer’s”. SVM of stage 1 classifier predicts with an accuracy of 0.86 and Random Forest with an accuracy of 0.71. Multinomial Logistic algorithm of stage 2 classifier accuracy is 0.89. 'e proposed work enables early prediction of a person at risk of Alzheimer’s Disease | Accuracy, sensitivity, specificity were reported for the tests and the models |
| 70 | Cooray et al. [84] | 2021 |
1) To investigate the possibility of using ML to identify the most important predictors of tooth loss 2) to predict the incidence of tooth loss 3) to understand the behaviour of those predictors |
19,407 older adult sfrom Japan Gerentological Evaluation Study (JAGES | Oral Diseases |
Feature selection—Boruta algorithm ML models- XGBoost classification algorithm, Random forest classification model |
XGBoost outperformed Random forest. Prediction of tooth loss was mainly influenced by older age, baseline oral health (having 10–19 teeth, wearing dentures), lower household income and manual occupations | XBoost (accuracy = 73.2%, F1 score = 21.4%, ROCAUC = 53.0%), RF (accuracy = 71.6%, F1 score = 25.3%, ROCAUC = 55.0%) |