A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study.

ABSTRACT: This study aims to identify depressive risks in elderly individuals with subjective cognitive decline (SCD) and develop a predictive model using machine learning algorithms to enable timely interventions.Data from the 2015 and 2018 waves of the China Health and Retirement Longitudinal Study (CHARLS) were used, including 1,921 elderly individuals. Depression was assessed with the CESD-10 scale. Three machine learning models-Gradient Boosting, Random Forest, and Boosted XGBoost-were used to predict depression risk over three years, incorporating 10 demographic, 5 health, 13 chronic disease, 3 lifestyle, and 2 physical function factors. Lasso feature selection identified 10 key variables for model training. Model performance was evaluated using ROC curves, AUC, sensitivity, specificity, accur

SUBMITTER: Xu J 

PROVIDER: S-EPMC12289894 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature