Identifying risk factors for depression and positive/negative mood changes in college students using machine learning.

ABSTRACT:

Background

In this study, machine learning was used to assess the prediction of the magnitude of depression changes in college students based on various psychological variable information.

Methods

A group of college students from a certain school completed two assessments in October 2021 and March 2022, respectively. We collected baseline levels of depression, demographic variables, parenting styles, college students' mental health information, personality information, coping styles, SCL-90, and social support information. We applied logistic regression, random forest, support vector machine, and k-nearest neighbor machine learning methods to predict the magnitude of depression changes in college students. We selected the best-performing model and outputted the importance of

SUBMITTER: Qiang Q 

PROVIDER: S-EPMC12283326 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature