English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This comprehensive review systematically evaluates Machine Learning (ML)methodologies employed in the detection, prediction, and analysis of mentalstress and its consequent mental disorders (MDs). Utilizing a rigorous scopingreview process, the investigation delves into the latest ML algorithms,preprocessing techniques, and data types employed in the context of stress andstress-related MDs. The findings highlight that Support Vector Machine (SVM),Neural Network (NN), and Random Forest (RF) models consistently exhibitsuperior accuracy and robustness among all machine learning algorithmsexamined. Furthermore, the review underscores that physiological parameters,such as heart rate measurements and skin response, are prevalently used asstress predictors in ML algorithms. This is attributed to their richexplanatory information concerning stress and stress-related MDs, as well asthe relative ease of data acquisition. Additionally, the application ofdimensionality reduction techniques, including mappings, feature selection,filtering, and noise reduction, is frequently observed as a crucial steppreceding the training of ML algorithms. The synthesis of this reviewidentifies significant research gaps and outlines future directions for thefield. These encompass areas such as model interpretability, modelpersonalization, the incorporation of naturalistic settings, and real-timeprocessing capabilities for detection and prediction of stress andstress-related MDs.

Machine Learning, Deep Learning and Data Preprocessing Techniques for  Detection, Prediction, and Monitoring of Stress and Stress-related Mental  Disorders: A Scoping Review