Predicting longitudinal depressive symptom trajectories among older Chinese adults with chronic health conditions: An interpretable machine learning study.

OBJECTIVE: This study leveraged interpretable machine learning (ML) to map heterogeneous trajectories of depressive symptoms in Chinese older adults with chronic diseases, aiming to develop an interpretable, prediction-oriented framework for personalized mental health interventions. METHODS: We analyzed four-wave longitudinal data from 5492 participants in the China Health and Retirement Longitudinal Study. Following trajectory identification, 10 ML algorithms were compared. A 50-iteration bootstrap Recursive Feature Elimination (RFE) distilled 10 core predictors from 39 baseline features. Models were evaluated using an 80:20 stratified split, with MICE imputation strictly preventing data leakage. Model interpretability was extracted via SHapley Additive exPlanations (SHAP). RESULTS: Three distinct trajectories emerged: Consistently Low, Chronically High, and Rapidly Escalating Risk. The Elastic Net model demonstrated optimal discriminative power (ROC-AUC = 0.710, PR-AUC = 0.468) and a Brier score of 0.337. SHAP analysis indicated that extremely low life satisfaction, severe instrumental functional limitations, and poor self-rated health were strongly associated with higher predicted probabilities of high-risk trajectories, whereas high household income robustly protected the low-risk group. We translated these insights into an interactive web-based risk calculator. CONCLUSION: This study identified critical depressive trajectory classes and established an interpretable predictive model. By translating complex analytics into an interpretable predictive framework, this approach provides a foundation for individualized risk profiling and highlights distinct patterns of risk that may inform future targeted mental health interventions for older adults with chronic conditions.