Machine Learning, Deep Learning and Data Preprocessing Techniques for
  Detection, Prediction, and Monitoring of Stress and Stress-related Mental
  Disorders: A Scoping Review
Moein Razavi Samira Ziyadidegan Reza Jahromi Saber Kazeminasab Vahid Janfaza Ahmadreza Mahmoudzadeh Elaheh Baharlouei Farzan Sasangohar
This comprehensive review systematically evaluates Machine Learning (ML)
methodologies employed in the detection, prediction, and analysis of mental
stress and its consequent mental disorders (MDs). Utilizing a rigorous scoping
review process, the investigation delves into the latest ML algorithms,
preprocessing techniques, and data types employed in the context of stress and
stress-related MDs. The findings highlight that Support Vector Machine (SVM),
Neural Network (NN), and Random Forest (RF) models consistently exhibit
superior accuracy and robustness among all machine learning algorithms
examined. Furthermore, the review underscores that physiological parameters,
such as heart rate measurements and skin response, are prevalently used as
stress predictors in ML algorithms. This is attributed to their rich
explanatory information concerning stress and stress-related MDs, as well as
the relative ease of data acquisition. Additionally, the application of
dimensionality reduction techniques, including mappings, feature selection,
filtering, and noise reduction, is frequently observed as a crucial step
preceding the training of ML algorithms. The synthesis of this review
identifies significant research gaps and outlines future directions for the
field. These encompass areas such as model interpretability, model
personalization, the incorporation of naturalistic settings, and real-time
processing capabilities for detection and prediction of stress and
stress-related MDs.