Examining factors for anxiety and depression prediction

Pandit, Malaika and Azwaan, Mohmmad and Wani, Sharyar and Adamu, Abubakar Ibrahim and Abdulghafor, Rawad Abdulkhaleq Abdulmolla and Gulzar, Yonis (2023) Examining factors for anxiety and depression prediction. International Journal on Perceptive and Cognitive Computing (IJPCC), 9 (1). pp. 70-79. E-ISSN 2462-229X

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Abstract

Mental health conditions, such as anxiety and depression, are a significant public health concern that can have significant impacts on an individual's quality of life, relationships, and overall well-being. In recent years, data science and machine learning techniques have emerged as important tools for early detection for mental health issues. This research aims at understanding the factors leading to anxiety and depression and implement predictive modelling for improving the accuracy and efficiency of early mental health diagnoses. Tabular DNN outperformed ANN and other machine learning classifiers by approximately 30%. Overall, our findings suggest that deep learning tabular models have the potential to improve the accuracy and efficiency. Thereby helping in early mental health diagnoses so that accessible and convenient support to individuals in need in context of this work.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Mental health, anxiety, depression, neural networks, DNN, ANN, classifiers
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Dr. Sharyar Wani
Date Deposited:	06 May 2025 09:27
Last Modified:	06 May 2025 09:27
URI:	http://irep.iium.edu.my/id/eprint/120785

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