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

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.