Transcultural prediction model for late-life depression based on multi-cohort machine learning and explainable AI.

BACKGROUND: Late-life depression is a global health concern with heterogeneous risk factors across populations. This study aimed to develop and validate a machine learning model for depression prediction in older adults using harmonized data from the United States and China.

METHODS: We harmonized data from the Health and Retirement Study (HRS, n = 6865) and China Health and Retirement Longitudinal Study (CHARLS, n = 4476) for adults aged ≥60 years. Depression was assessed using validated scales in both cohorts. The Boruta algorithm was used for feature selection. 17 machine learning algorithms were evaluated, with HRS data split into training (70 %) and internal validation (30 %), and CHARLS data used for external validation. Model performance was assessed using AUC, decision curve analysis, calibration plots, and SHapley Additive Explanations (SHAP).

RESULTS: The Gradient Boosting Machine (GBM) model achieved the best performance, with AUCs of 0.752 (95 % CI: 0.735-0.768) in HRS training, 0.763 (95 % CI: 0.737-0.788) in HRS validation, and 0.717 (95 % CI: 0.702-0.732) in CHARLS validation. The model showed good calibration and positive net benefit across relevant clinical thresholds. SHAP analysis identified self-rated health, functional dependency, self-rated memory, arthritis, and ADL score as top predictors with consistent effects across populations.

CONCLUSION: We developed a robust and interpretable machine learning model for predicting late-life depression that generalizes across culturally distinct populations. The results highlight both common predictive factors and the need for population-specific considerations in clinical application.