Suicidal behaviour prediction models using machine learning techniques: A systematic review.

Journal: Artificial intelligence in medicine
PMID: 36207078

Abstract

BACKGROUND: Early detection and prediction of suicidal behaviour are key factors in suicide control. In conjunction with recent advances in the field of artificial intelligence, there is increasing research into how machine learning can assist in the detection, prediction and treatment of suicidal behaviour. Therefore, this study aims to provide a comprehensive review of the literature exploring machine learning techniques in the study of suicidal behaviour prediction.

Authors

  • Noratikah Nordin
    School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia. Electronic address: noratikahnordin@student.usm.my.
  • Zurinahni Zainol
    School of Computer Sciences, Universiti Sains Malaysia, 11800 Gelugor, Malaysia.
  • Mohd Halim Mohd Noor
    School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia. Electronic address: halimnoor@usm.my.
  • Lai Fong Chan
    Department of Psychiatry, Faculty of Medicine, National University of Malaysia (UKM) 56000 Cheras, Wilayah Persekutuan Kuala Lumpur, Malaysia.

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