A comprehensive review of predictive analytics models for mental illness using machine learning algorithms
Our emotional, psychological, and social well-being are all parts of our mental health, influencing our thoughts, emotions, and behaviors. Mental health also influences how we respond to stress, interact with others, and make good or bad decisions. There has been growing interest in the use of machi...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442524000522 |
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| author | Md. Monirul Islam Shahriar Hassan Sharmin Akter Ferdaus Anam Jibon Md. Sahidullah |
| author_facet | Md. Monirul Islam Shahriar Hassan Sharmin Akter Ferdaus Anam Jibon Md. Sahidullah |
| author_sort | Md. Monirul Islam |
| collection | DOAJ |
| description | Our emotional, psychological, and social well-being are all parts of our mental health, influencing our thoughts, emotions, and behaviors. Mental health also influences how we respond to stress, interact with others, and make good or bad decisions. There has been growing interest in the use of machine learning for the early detection of mental illness. This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. We further propose a comprehensive methodology for assessing mental health that synergistically combines social media monitoring, data analytics from wearable devices, verbal polls, and individualized support. We provide an overview of the field’s current state, highlight the potential benefits and challenges of using machine learning in mental health care, and a new taxonomy of mental disorders issues based on five domains of data types. We review existing research on using machine learning to detect and treat mental illness and discuss the implications for future research. Finally, the value of this work lies in its potential to provide a fast and accurate method for predicting the mental health status of a person, which may assist in the diagnosis and treatment of mental illness. |
| format | Article |
| id | doaj-art-71092c5451c743659ec49dbdd2a1b73b |
| institution | OA Journals |
| issn | 2772-4425 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-71092c5451c743659ec49dbdd2a1b73b2025-08-20T02:32:12ZengElsevierHealthcare Analytics2772-44252024-12-01610035010.1016/j.health.2024.100350A comprehensive review of predictive analytics models for mental illness using machine learning algorithmsMd. Monirul Islam0Shahriar Hassan1Sharmin Akter2Ferdaus Anam Jibon3Md. Sahidullah4Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka 1216, Bangladesh; Rising Research Lab, Sheikh Bari, Malancha, Melandah, Jamalpur, Mymensingh 2012, Bangladesh; Corresponding author.Department of Computer Science and Engineering, Gono Bishwabidyalay, Savar, Dhaka 1344, BangladeshDepartment of Computer Science and Engineering, Atish Dipankar University of Science and Technology, Dhaka, BangladeshDepartment of Computer Science and Engineering, IUBAT - International University of Business Agriculture and Technology, Dhaka, BangladeshDepartment of Computer Science and Engineering, Asian University of Bangladesh (AUB), Bangabandhu Road, Tongabari Ashulia, Dhaka 1349, BangladeshOur emotional, psychological, and social well-being are all parts of our mental health, influencing our thoughts, emotions, and behaviors. Mental health also influences how we respond to stress, interact with others, and make good or bad decisions. There has been growing interest in the use of machine learning for the early detection of mental illness. This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. We further propose a comprehensive methodology for assessing mental health that synergistically combines social media monitoring, data analytics from wearable devices, verbal polls, and individualized support. We provide an overview of the field’s current state, highlight the potential benefits and challenges of using machine learning in mental health care, and a new taxonomy of mental disorders issues based on five domains of data types. We review existing research on using machine learning to detect and treat mental illness and discuss the implications for future research. Finally, the value of this work lies in its potential to provide a fast and accurate method for predicting the mental health status of a person, which may assist in the diagnosis and treatment of mental illness.http://www.sciencedirect.com/science/article/pii/S2772442524000522Machine learning classificationHealthcareMental illness predictionMental disorder taxonomyDiagnosisMental illness treatment |
| spellingShingle | Md. Monirul Islam Shahriar Hassan Sharmin Akter Ferdaus Anam Jibon Md. Sahidullah A comprehensive review of predictive analytics models for mental illness using machine learning algorithms Healthcare Analytics Machine learning classification Healthcare Mental illness prediction Mental disorder taxonomy Diagnosis Mental illness treatment |
| title | A comprehensive review of predictive analytics models for mental illness using machine learning algorithms |
| title_full | A comprehensive review of predictive analytics models for mental illness using machine learning algorithms |
| title_fullStr | A comprehensive review of predictive analytics models for mental illness using machine learning algorithms |
| title_full_unstemmed | A comprehensive review of predictive analytics models for mental illness using machine learning algorithms |
| title_short | A comprehensive review of predictive analytics models for mental illness using machine learning algorithms |
| title_sort | comprehensive review of predictive analytics models for mental illness using machine learning algorithms |
| topic | Machine learning classification Healthcare Mental illness prediction Mental disorder taxonomy Diagnosis Mental illness treatment |
| url | http://www.sciencedirect.com/science/article/pii/S2772442524000522 |
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