Mental Health Prediction Models Using Machine Learning in Higher Education Institution

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Sofianita Mutalib, et. al.

Abstract

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.

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