Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data.

ABSTRACT:

Background

The advancement of artificial intelligence technologies has opened new avenues for depression prevention and management in older adults with disability (defined by basic or instrumental activities of daily living, BADL/IADL). This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.

Methods

This study utilized longitudinal data from the CHARLS 2011-2015 cohort. A three-stage serial consensus approach feature selection framework (LASSO, Elastic Net, and Boruta) was employed to identify 21 robust predictors from 74 candidate variables. Ten ML algorit

SUBMITTER: Jin T 

PROVIDER: S-EPMC12263909 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature