Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.

Journal: JMIR mental health
Published Date: Aug 21, 2024

Abstract

BACKGROUND: Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.

Authors

  • Moein Razavi
    Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
  • Samira Ziyadidegan
    Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
  • Ahmadreza Mahmoudzadeh
    Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, United States.
  • Saber Kazeminasab
    Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
  • Elaheh Baharlouei
    Department of Computer Science, University of Houston, Houston, TX, United States.
  • Vahid Janfaza
    Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States.
  • Reza Jahromi
    Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
  • Farzan Sasangohar
    Mike and Sugar Barnes Faculty Fellow II, Wm Michael Barnes and Department of Industrial and Systems Engineering at Texas A&M University, College Station, TX, 77843, USA.

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