This paper is in the following e-collection/theme issue:

Reviews in Digital Mental Health (256) Machine Learning (1627) Anxiety and Stress Disorders (1138) Diagnostic Tools in Mental Health (272) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1701) Tools, Programs and Algorithms (305)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53714, first published .
Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review

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

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

Authors of this article:

Moein Razavi1, 2 Author Orcid Image ;   Samira Ziyadidegan1 Author Orcid Image ;   Ahmadreza Mahmoudzadeh3 Author Orcid Image ;   Saber Kazeminasab4 Author Orcid Image ;   Elaheh Baharlouei5 Author Orcid Image ;   Vahid Janfaza2 Author Orcid Image ;   Reza Jahromi1, 2 Author Orcid Image ;   Farzan Sasangohar1 Author Orcid Image
Article Authors Cited by (6) Tweetations Metrics

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  2. . Advances in Wearable Sensor-Based Machine Learning for Mental Stress Detection: Techniques, Challenges, and Future Directions. REST Journal on Data Analytics and Artificial Intelligence 2023;3(3):130 View
  3. N K, C A, N S, B R. Advancements in Machine Learning-based Predictive Models for Bipolar Disorder Episodes. Journal of Soft Computing Paradigm 2024;6(4):350 View
  4. Alkurdi A, Clore J, Sowers R, Hsiao-Wecksler E, Hernandez M. Resilience of Machine Learning Models in Anxiety Detection: Assessing the Impact of Gaussian Noise on Wearable Sensors. Applied Sciences 2024;15(1):88 View
  5. Nartey D, Karthikeyan R, Chaspari T, Mehta R. Exploring the role of cardiac activity in forecasting cognitive fatigue with machine learning. IISE Transactions on Healthcare Systems Engineering 2025:1 View
  6. Choomung P, He Y, Matsunaga M, Sakuma K, Kishi T, Li Y, Tanihara S, Iwata N, Ota A. Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network–Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey. JMIR Formative Research 2025;9:e66330 View