Predictors of depression among Chinese college students: a machine learning approach.

ABSTRACT:

Background

Depression is highly prevalent among college students, posing a significant public health challenge. Identifying key predictors of depression is essential for developing effective interventions. This study aimed to analyze potential depression risk factors among Chinese college students using the Random Forest Algorithm (RFA) and to explore gender differences in risk patterns.

Methods

A cross-sectional study was conducted with 10,043 undergraduate students from Guizhou Normal University. Thirty-three variables were analyzed using RFA. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), with a score of ≥ 16 indicating depression risk. The variables included sociodemographic characteristics, physical and psychologic

SUBMITTER: Luo L 

PROVIDER: S-EPMC11800555 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature