biostudies-literatureXu RFMajor Humanities and Social Sciences Research Projects in Zhejiang higher education institutionsNational Social Science Fund of China286https://www.ebi.ac.uk/biostudies/studies/S-EPMC11938587biostudies-literatureUnknown25(1)<h4>Objective</h4>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.<h4>Methods</h4>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 throuBMC psychiatryMachine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.PMC119385872024GH03623BSH135Ouyang SLiu ZJYan WJXu RFXu DWDong QfalseMachine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.<h4>Objective</h4>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.<h4>Methods</h4>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 throu2025-01-01T00:00:00Z2025 Mar2025-07-04T03:05:42.976Z2025-07-04T03:05:42.976ZS-EPMC119385874013384810.1186/s12888-025-06693-8