ArticleBMC psychiatry2025
Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Longitudinal Association Between Physical Exercise and Depressive Symptoms in Older Adults: The Prospective Explanatory Role of Loneliness and the Moderating Role of Cognitive Emotion Regulation.Behavioral sciences (Basel, Switzerland) · 2026Article
- Heterogeneous and nonlinear associations between depressive symptoms and functional disability in older Chinese adults.BMC psychology · 2026Article
- Construction of a prediction model for depression risk in elderly patients with chronic obstructive pulmonary disease based on machine learning algorithms and analysis of influencing factors.Scientific reports · 2026Article
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- Depressive trajectories and their associations with chronic diseases among older adults: a national longitudinal cohort study from China.BMC geriatrics · 2025Article
- Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.BMC psychiatry · 2025Article
- Dynamic nomogram for predicting depression risk in middle-aged and older adults based on CHARLS.Scientific reports · 2025Article
- Temporal trends and machine learning prediction of depressive symptoms among Chinese middle-aged and elderly individuals: a national cohort study.BMC public health · 2025Article
- Artificial neural network-driven approaches to improved forecasting of disability care expenditures in an aging Kingdom of Saudi Arabia population.Scientific reports · 2025Article
- Article
- The relationship between chronic pain and health-related quality of life: the mediating roles of frailty and depression in chronic disease patients.Frontiers in public health · 2025Article
- Explaining factors influencing students' depression with a deep learning approach.Frontiers in psychology · 2025Article
- Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data.Frontiers in artificial intelligence · 2025Article
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4 authors.
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Abstract
backgroundGiven the accelerated aging population in China, the number of disabled elderly individuals is increasing, and depression is a common mental disorder among older adults. This study aims to establish an effective model for predicting depression risks among disabled elderly individuals.
methodsThe data for this study was obtained from the 2018 China Health and Retirement Longitudinal Study (CHARLS). In this study, disability was defined as a functional impairment in at least one activity of daily living (ADL) or instrumental activity of daily living (IADL). Depressive symptoms were assessed by using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D10). We employed SPSS 27.0 to select independent risk factor variables associated with depression among disabled elderly individuals. Subsequently, a predictive model for depression in this population was constructed using R 4.3.0. The model's discrimination, calibration, and clinical net benefits were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curves.
resultsIn this study, 3,107 elderly individuals aged 60 years and older with disabilities were included. Poor self-rated health, pain, absence of caregivers, cognitive impairment, and shorter sleep duration were identified as independent risk factors for depression in disabled elderly individuals. The XGBoost model demonstrated superior performance in the training set, while the logistic regression model outperformed it in the validation set, with AUCs of 0.76 and 0.73, respectively. The calibration curve and Brier score (Brier: 0.20) indicated a good model fit. Moreover, decision curve analysis confirmed the clinical utility of the model.
conclusionsThe predictive model exhibits outstanding predictive efficacy, greatly assisting healthcare professionals and family members in evaluating depression risks among disabled elderly individuals. Consequently, it enables the early identification of elderly individuals at high risk for depression.
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