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Depression prediction using machine learning: a review

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 identif...

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Main Authors: Abdul Rahimapandi, Hanis Diyana, Maskat, Ruhaila, Musa, Ramli, Ardi, Norizah
Format: Article
Language:English
Published: Intelektual Pustaka Media Utama 2022
Subjects:
H61.8 Communication of information
RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Online Access:http://irep.iium.edu.my/97159/3/97159_Depression%20prediction%20using%20machine%20learning%20a%20review.pdf
http://irep.iium.edu.my/97159/
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spelling my.iium.irep.971592022-03-14T04:38:51Z http://irep.iium.edu.my/97159/ Depression prediction using machine learning: a review Abdul Rahimapandi, Hanis Diyana Maskat, Ruhaila Musa, Ramli Ardi, Norizah H61.8 Communication of information RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry 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. Intelektual Pustaka Media Utama 2022-03-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/97159/3/97159_Depression%20prediction%20using%20machine%20learning%20a%20review.pdf 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) http://ijai.iaescore.com 10.11591/ijai
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic H61.8 Communication of information
RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
spellingShingle H61.8 Communication of information
RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Abdul Rahimapandi, Hanis Diyana
Maskat, Ruhaila
Musa, Ramli
Ardi, Norizah
Depression prediction using machine learning: a review
description 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.
format Article
author Abdul Rahimapandi, Hanis Diyana
Maskat, Ruhaila
Musa, Ramli
Ardi, Norizah
author_facet Abdul Rahimapandi, Hanis Diyana
Maskat, Ruhaila
Musa, Ramli
Ardi, Norizah
author_sort Abdul Rahimapandi, Hanis Diyana
title Depression prediction using machine learning: a review
title_short Depression prediction using machine learning: a review
title_full Depression prediction using machine learning: a review
title_fullStr Depression prediction using machine learning: a review
title_full_unstemmed Depression prediction using machine learning: a review
title_sort depression prediction using machine learning: a review
publisher Intelektual Pustaka Media Utama
publishDate 2022
url http://irep.iium.edu.my/97159/3/97159_Depression%20prediction%20using%20machine%20learning%20a%20review.pdf
http://irep.iium.edu.my/97159/
http://ijai.iaescore.com
_version_ 1728051167446958080
score 13.211869

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