Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data
BackgroundThe advancement of artificial intelligence technologies has opened new avenues for depression prevention and management in older adults with disability (defined by basic or instrumental activities of daily living, BADL/IADL). This study systematically developed machine learning (ML) models...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1624171/full |
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| Summary: | BackgroundThe advancement of artificial intelligence technologies has opened new avenues for depression prevention and management in older adults with disability (defined by basic or instrumental activities of daily living, BADL/IADL). This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.MethodsThis study utilized longitudinal data from the CHARLS 2011–2015 cohort. A three-stage serial consensus approach feature selection framework (LASSO, Elastic Net, and Boruta) was employed to identify 21 robust predictors from 74 candidate variables. Ten ML algorithms were evaluated: LR, HistGBM, MLP, XGBoost, bagging, DT, LightGBM, RF, SVM, and CatBoost. Temporal external validation was performed using an independent 2018–2020 cohort to assess model generalizability. Performance was comprehensively evaluated using accuracy, AUC, F1-score, precision, and recall metrics. The SHAP framework was employed to interpret feature contribution mechanisms.ResultsResults demonstrated that the HistGBM model achieved optimal overall performance on the testing sets (AUC = 0.779, F1-score = 0.735, accuracy = 0.713), with only an 8.5% AUC difference between training and testing sets and a 10% difference between external validation and testing sets, indicating temporal stability. SHAP interpretability analysis revealed that sleep time (mean SHAP value = 0.344) in the health behavior domain and life satisfaction (0.339) and episodic memory (0.220) in the subjective perception domain contributed more significantly to prediction than traditional biomedical indicators.ConclusionThis study developed an AI-based tool for depression risk assessment in older adults with disability through a multi-stage feature selection process and a temporal external validation framework. These findings provide a practical screening instrument and a methodological reference for implementing AI technologies in geriatric mental health applications, thereby facilitating clinical translation of predictive analytics in this field. |
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| ISSN: | 2624-8212 |