What question did this study set out to answer?

To develop a machine learning model for predicting depression risk in rural older adults using various factors.

February 16, 2026Open Access

Development and validation of a machine learning model for predicting depression risk in rural Chinese older adults: Evidence from the CHARLS cohort

Key Points

To develop a machine learning model for predicting depression risk in rural older adults using various factors.
Analyzed data from 3232 rural older adults in CHARLS study.
Assessed depressive symptoms using CES-D10 scale.
Applied LASSO regression for predictor selection.
Compared six machine learning algorithms including DMR-CNN and SVM.
Evaluated model performance using ROC curves and calibration plots.
38.9% of participants showed depressive symptoms.
Identified nine key predictors for depression risk.
DMR-CNN achieved the highest accuracy of 0.875 with AUCs between 0.788 and 0.899.
Sensitivity of DMR-CNN was 0.852.
Lowest Brier score was 0.112, indicating good prediction quality.

Abstract

Background Depression poses a serious threat to the well-being of older adults, especially in rural China, where healthcare resources are limited. This study aimed to develop a machine learning model incorporating social, psychological, and physiological factors to predict depression risk among rural elderly individuals, supporting early screening and intervention. Methods A total of 3232 rural older adults from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS) were included. Depressive symptoms were assessed using the CES-D10 scale. LASSO regression was applied to select predictors. Six machine learning algorithms—SVM, DMR-CNN, DT, XGBoost, RF, and LR—were compared. Model performance was evaluated by ROC curves, calibration plots, and decision curve analysis. Results Among participants, 1259 (38.9%) showed depressive symptoms. Nine predictors were selected. DMR-CNN outperformed other models, achieving AUCs between 0.788 and 0.899, the highest accuracy of 0.875, a sensitivity of 0.852, and the lowest Brier score of 0.112. Conclusion Machine learning models based on CHARLS data show potential to identify depression risk in rural older adults. Key risk factors include older age, female sex, chronic disease, pain, poor sleep, and cognitive decline. These findings support precise and early mental health interventions in underserved aging populations.

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