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Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect

Introduction; The validated Depression, Anxiety and Stress Scale, 21 items (DASS-21) offers an insight on categorizing individuals into severity of each condition. The advancement in public health big data provides a platform for early detection and prompt treatment of individuals. However, ther...

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Main Authors: Mohammad Aidid, Edre, Musa, Ramli
Format: Conference or Workshop Item
Language:English
English
Published: 2021
Subjects:
RA644.3 Chronic and Noninfectious Diseases and Public Health
RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Online Access:http://irep.iium.edu.my/90102/1/ID%20166%20POSTER%203WCII.pdf
http://irep.iium.edu.my/90102/7/Abstract%20Book%203WCII.pdf
http://irep.iium.edu.my/90102/
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spelling my.iium.irep.901022021-07-30T04:44:34Z http://irep.iium.edu.my/90102/ Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect Mohammad Aidid, Edre Musa, Ramli RA644.3 Chronic and Noninfectious Diseases and Public Health RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry Introduction; The validated Depression, Anxiety and Stress Scale, 21 items (DASS-21) offers an insight on categorizing individuals into severity of each condition. The advancement in public health big data provides a platform for early detection and prompt treatment of individuals. However, there are lacking evidence on prediction accuracy of these data using artificial intelligence methods. Objectives 1. To determine accuracy of supervised machine learning in predicting depression, anxiety and stress using big data. 2. To determine the most important predictor of depression, anxiety and stress using machine learning model Method; Cross sectional study using secondary data obtained from an online resource center was conducted, involving 339,781 respondents. Outcomes were depression, anxiety and stress were measured using DASS21. Each outcome was modelled with the rest of the outcome, plus gender, age, race, marital status, education level and occupational status. Feed-forward artificial neural network was modelled using multilayer perceptron machine learning procedure using IBM SPSS version 2 2021 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/90102/1/ID%20166%20POSTER%203WCII.pdf application/pdf en http://irep.iium.edu.my/90102/7/Abstract%20Book%203WCII.pdf Mohammad Aidid, Edre and Musa, Ramli (2021) Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect. In: 3rd World Congress on Integration and Islamicisation: Mental Health and Well-Being in the 4th Industrial Revolution, 4th-6th June 2021, Kuantan, Pahang. (Unpublished)
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
English
topic RA644.3 Chronic and Noninfectious Diseases and Public Health
RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
spellingShingle RA644.3 Chronic and Noninfectious Diseases and Public Health
RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Mohammad Aidid, Edre
Musa, Ramli
Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
description Introduction; The validated Depression, Anxiety and Stress Scale, 21 items (DASS-21) offers an insight on categorizing individuals into severity of each condition. The advancement in public health big data provides a platform for early detection and prompt treatment of individuals. However, there are lacking evidence on prediction accuracy of these data using artificial intelligence methods. Objectives 1. To determine accuracy of supervised machine learning in predicting depression, anxiety and stress using big data. 2. To determine the most important predictor of depression, anxiety and stress using machine learning model Method; Cross sectional study using secondary data obtained from an online resource center was conducted, involving 339,781 respondents. Outcomes were depression, anxiety and stress were measured using DASS21. Each outcome was modelled with the rest of the outcome, plus gender, age, race, marital status, education level and occupational status. Feed-forward artificial neural network was modelled using multilayer perceptron machine learning procedure using IBM SPSS version 2
format Conference or Workshop Item
author Mohammad Aidid, Edre
Musa, Ramli
author_facet Mohammad Aidid, Edre
Musa, Ramli
author_sort Mohammad Aidid, Edre
title Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
title_short Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
title_full Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
title_fullStr Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
title_full_unstemmed Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
title_sort supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
publishDate 2021
url http://irep.iium.edu.my/90102/1/ID%20166%20POSTER%203WCII.pdf
http://irep.iium.edu.my/90102/7/Abstract%20Book%203WCII.pdf
http://irep.iium.edu.my/90102/
_version_ 1706956579965763584
score 13.211869

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