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

Journal: BMC public health
PMID: 39910488

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.

Authors

  • Lin Luo
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Junfeng Yuan
    School of Physical Education, Guizhou Normal University, Guiyang, 550075, China.
  • Chenghan Wu
    Guizhou Center for Disease Control and Prevention, Guiyang, 550001, China.
  • Yanling Wang
    Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
  • Rui Zhu
    Department of Urology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510700, China.
  • Huilin Xu
    Department of Radiology, Second Affiliated Hospital, Army Medical University, Chongqing, 400037, P. R. China.
  • Luqin Zhang
    School of Physical Education, Guizhou Normal University, Guiyang, 550075, China.
  • Zhongge Zhang
    School of Physical Education, Guizhou Normal University, Guiyang, 550075, China.

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