  Background: Mental stress and its consequent mental disorders (MDs) are
significant public health issues. With the advent of machine learning (ML),
there's potential to harness computational techniques for better understanding
and addressing these problems. This review seeks to elucidate the current ML
methodologies employed in this domain to enhance the detection, prediction, and
analysis of mental stress and MDs.
  Objective: This review aims to investigate the scope of ML methodologies used
in the detection, prediction, and analysis of mental stress and MDs.
  Methods: Utilizing a rigorous scoping review process with PRISMA-ScR
guidelines, this investigation delves into the latest ML algorithms,
preprocessing techniques, and data types used in the context of stress and
stress-related MDs.
  Results and Discussion: A total of 98 peer-reviewed publications were
examined. The findings highlight that Support Vector Machine (SVM), Neural
Network (NN), and Random Forest (RF) models consistently exhibit superior
accuracy and robustness among ML algorithms. Physiological parameters such as
heart rate measurements and skin response are prevalently used as stress
predictors due to their rich explanatory information and ease of data
acquisition. Dimensionality reduction techniques, including mappings, feature
selection, filtering, and noise reduction, are frequently observed as crucial
steps preceding the training of ML algorithms.
  Conclusion: This review identifies significant research gaps and outlines
future directions for the field. These include model interpretability, model
personalization, the incorporation of naturalistic settings, and real-time
processing capabilities for the detection and prediction of stress and
stress-related MDs.
  Keywords: Machine Learning; Deep Learning; Data Preprocessing; Stress
Detection; Stress Prediction; Stress Monitoring; Mental Disorders
