Mental health prediction models using machine learning in higher education institution

Mental health prediction models using machine learning in higher education institution. Turkish Journal of Computer and Mathematics Education, 12 (5). pp. 1782-1792. ISSN 1309-4653 (2021)


Today, mental health problem has become a grave concern in Malaysia. According to the National Health and Morbidity Survey (NHMS) 2017, one in five people in Malaysia suffers from depression, two in five from anxiety, and one in ten from stress. Higher education students are also at risk of being part of the affected community. The increased data size without proper management and analysis, and the lack of counsellors, are compounding the issue. Therefore, this paper presents on identifying factors in mental health problems among selected higher education students. This study aims to classify students into different categories of mental health problems, which are stress, depression, and anxiety, using machine learning algorithms. The data is collected from students in a higher education institute in Kuala Terengganu. The algorithms applied are Decision Tree, Neural Network, Support Vector Machine, Naïve Bayes, and logistic regression. The most accurate model for stress, depression, and anxiety is Decision Tree, Support Vector Machine, and Neural Network, respectively.

Item Type: Article
Keywords: Machine learning, Algorithm analysis, Higher Institution, Mental Health, Anxiety, Stress, Depression
Taxonomy: By Subject > Computer & Mathematical Sciences > Computer Science
By Subject > Medicine > Psychiatry
Local Content Hub: Subjects > Computer and Mathematical Sciences
Depositing User: Mohd Fadhli Samsudin
Date Deposited: 31 Jan 2022 23:46
Last Modified: 03 Feb 2022 08:16
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