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

Mohammad Aidid, Edre and Musa, Ramli (2022) Accuracy of supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect. Malaysian Journal of Medicine and Health Sciences, 18 (Supp 19). pp. 87-92. E-ISSN 2636-9346

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Abstract

Introduction: One of the most useful tool to assess the extent of depression, anxiety and stress symptoms is the val�idated Depression, Anxiety and Stress Scale, 21 items (DASS-21). The availability of online mental health resource centre provides big data capable of machine learning analytics for early detection of mental health issues. Howev�er, prediction accuracy of these data using machine learning method remains elusive. Methods: A cross sectional study was conducted, using secondary data of respondents who answered an online DASS-21 questionnaire from an online resource center. Depression, anxiety and stress were measured using DASS21 as either the outcome or predictor, depending on the model. The model includes sociodemographic predictors such as gender, age, race, marital status, education level and occupational status. A feed-forward artificial neural network was constructed based on multilayer perceptron machine learning procedure using IBM SPSS version 23. Results: A total of 339,781 respondents data were obtained. The observed prevalence of depression, anxiety and stress was 39.9%, 48.5% and 13.4%, respectively. This resulted in 76.4% prediction accuracy for depression, 76.3% accuracy for anxiety and 87.4% prediction accuracy for stress. Stress and anxiety were the most important factors contributing to the disease model. Conclusion: The prediction models have high accuracy to predict the true observed depression, anxiety and stress prevalence. The clinical relevance of these prediction models still needs the human intellect judgment based on Maqasid al-Shariah principles. Machine learning therefore should not be abused but to help in decision-making towards early detection and prompt treatment.

Item Type:	Article (Journal)
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 > RA Public aspects of medicine > RA790 Mental Health. Mental Illness Prevention
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Medicine > Department of Community Medicine (Effective: 1st January 2011) Kulliyyah of Medicine > Department of Psychiatry Kulliyyah of Medicine
Depositing User:	Dr Edre Mohammad Aidid
Date Deposited:	25 Jan 2023 11:04
Last Modified:	25 Jan 2023 11:05
URI:	http://irep.iium.edu.my/id/eprint/103379

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