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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2023
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| Online Access: | https://arxiv.org/abs/2308.04616 |
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| _version_ | 1866916314406191104 |
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| author | Razavi, Moein Ziyadidegan, Samira Jahromi, Reza Kazeminasab, Saber Janfaza, Vahid Mahmoudzadeh, Ahmadreza Baharlouei, Elaheh Sasangohar, Farzan |
| author_facet | Razavi, Moein Ziyadidegan, Samira Jahromi, Reza Kazeminasab, Saber Janfaza, Vahid Mahmoudzadeh, Ahmadreza Baharlouei, Elaheh Sasangohar, Farzan |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_04616 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review Razavi, Moein Ziyadidegan, Samira Jahromi, Reza Kazeminasab, Saber Janfaza, Vahid Mahmoudzadeh, Ahmadreza Baharlouei, Elaheh Sasangohar, Farzan Machine Learning 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 |
| title | Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2308.04616 |