Mental health prediction using machine learning: taxonomy,applications, and challenges

Jetli Chung and Jason Teo (2022) Mental health prediction using machine learning: taxonomy,applications, and challenges. Applied Computational Intelligence and So Computing, 2022. pp. 1-19.

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Abstract

The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. )is paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. )en, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.

Item Type:	Article
Keyword:	Taxonomy , mental health , schizophrenia
Subjects:	Q Science > QR Microbiology > QR1-502 Microbiology > QR75-99.5 Bacteria R Medicine > RC Internal medicine > RC31-1245 Internal medicine > RC321-571 Neurosciences. Biological psychiatry. Neuropsychiatry > RC346-429 Neurology. Diseases of the nervous system Including speech disorders > RC435-571 Psychiatry > RC475-489 Therapeutics. Psychotherapy
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	16 Jul 2025 17:17
Last Modified:	16 Jul 2025 17:17
URI:	https://eprints.ums.edu.my/id/eprint/44521

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