Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: A cross-sectional study.

Journal: Journal of affective disorders
PMID: 36122602

Abstract

BACKGROUND: Machine learning (ML) algorithms based on various clinicodemographic, psychometric, and biographic factors have been used to predict depression, suicidal ideation, and suicide attempt in adolescents, but there is still a need for more accurate and efficient models for screening the general adolescent population. In this study, we compared various ML methods to identify a model that most accurately predicts suicidal ideation and level of depression in a large cohort of school-aged adolescents.

Authors

  • Yating Huang
    School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
  • Chunyan Zhu
    Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, 361102, China.
  • Yu Feng
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Yifu Ji
    Psychiatry Department of Hefei Fourth People's Hospital, Hefei, China.
  • Jingze Song
    Institute of Affective Computing Department of Computer Science and Technology, Hefei University of Technology, Hefei, China.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Fengqiong Yu
    School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China; The Second Affiliated Hospital of Anhui Medical University, Hefei, China. Electronic address: yufengqiong@ahmu.edu.cn.

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