IIUM Repository

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

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 (Unpublished)

[img] PDF - Presentation
Restricted to Registered users only

Download (629kB) | Request a copy
[img] PDF (Abstract book) - Supplemental Material
Restricted to Registered users only

Download (1MB) | Request a copy

Abstract

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

Item Type: Conference or Workshop Item (Poster)
Additional Information: Conference was held online
Uncontrolled Keywords: Supervised Machine Learning, Depression, Anxiety, Stress, Big Data
Subjects: R Medicine > RA Public aspects of medicine > RA644.3 Chronic and Noninfectious Diseases and Public Health
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Medicine > Department of Psychiatry
Kulliyyah of Medicine
Depositing User: Professor Ramli Musa
Date Deposited: 30 Jul 2021 12:44
Last Modified: 30 Jul 2021 12:44
URI: http://irep.iium.edu.my/id/eprint/90102

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year