Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.

ABSTRACT:

Objective

To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while maintaining diagnostic accuracy, addressing the efficiency limitations in large-scale applications.

Methods

Data were derived from the Chinese Psychological Health Guard Project (primary sample: n = 179,877; age 9-18) and China Labor-force Dynamics Survey (validation samples across age spans). We employed a two-stage machine learning approach: first applying Recursive Feature Elimination with multiple linear regression to identify core predictive items for total depression scores, followed by logistic regression for optimizing depression classification (CES-D ≥ 16). Model performance was systematically evaluated throu

SUBMITTER: Xu RF 

PROVIDER: S-EPMC11938587 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature