IIUM Repository

Depression prediction using machine learning: a review

Abdul Rahimapandi, Hanis Diyana and Maskat, Ruhaila and Musa, Ramli and Ardi, Norizah (2022) Depression prediction using machine learning: a review. International Journal of Artificial Intelligence (IJ-AI), 99 (1). pp. 1-11. ISSN 2252-8938 (In Press)

PDF - Published Version
Download (638kB) | Preview
Official URL: http://ijai.iaescore.com


Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases from 2016 to 2020 that use machine learning techniques to predict depression. The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. This understanding provides researchers with the fundamental components essential to predict depression. Fifteen articles were found relevant. We based our review on the Systematic Mapping Study (SMS) method. Three research questions were answered through this review. We discovered that sixteen variables were deemed important by the literature. Not all of the reviewed literature utilizes depression screening tools in the prediction process. Nevertheless, from the five screening tools discovered, the most frequently used were Hospital Anxiety and Depression Scale (HADS) and Hamilton Depression Rating Scale (HDRS) for general population, while for literature targeting older population Geriatric Depression Scale GDS was often employed. A total of twenty-two machine learning algorithms were identified employed to predict depression and Random Forest was found to be the most reliable algorithm across the publications.

Item Type: Article (Review)
Additional Information: 5818/97159
Uncontrolled Keywords: Machine learning, Depression, Prediction, Literature review
Subjects: H Social Sciences > H Social Sciences (General) > H61.8 Communication of information
R Medicine > RC Internal medicine > RC321 Neuroscience. Biological psychiatry. Neuropsychiatry
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Medicine > Department of Psychiatry
Depositing User: Professor Ramli Musa
Date Deposited: 14 Mar 2022 12:38
Last Modified: 14 Mar 2022 12:38
URI: http://irep.iium.edu.my/id/eprint/97159

Actions (login required)

View Item View Item


Downloads per month over past year