ISSN: 2222-6990
Open access
The sudden shift to online learning caused by the COVID-19 pandemic has had a significant impact on students' mental health. Therefore, this paper presents a research project that utilized machine learning techniques to classify students' mental health during the COVID-19 pandemic and developed a web-based interactive dashboard for interactive data visualization. The project used Python as the programming language and adopted various machine learning models such as Support Vector Machine, Decision Tree, and Random Forest. The findings of the research reveal that the Support Vector Machine algorithm is the best algorithm in the mental health classification model, with an accuracy above 90%, as measured by a confusion matrix. The application of web-based interactive dashboard for data visualization assists educational institutions in identifying and supporting struggling students while developing effective mental health support strategies beyond the pandemic. While the project has limitations, including the inability to determine the root causes of mental health issues due to insufficient variables, it presents the immense potential of machine learning tools in addressing and mitigating mental health challenges during crises like the COVID-19 pandemic. Machine learning-based predictions can serve as valuable tools for mental health professionals in their decision-making processes, and interventions for students who may be at higher risk or in need of immediate support.
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In-Text Citation: (Masrom et al., 2023)
To Cite this Article: Masrom, S., Jamaludin, N. F., Razak, F. A., & Ismail, N. R. P. @. (2023). Machine Learning Approach to Classify Students’ Mental Health During the COVID-19 Pandemic: A Web-Based Interactive Dashboard. International Journal of Academic Research in Business and Social Sciences, 13(7), 1192 – 1204.
Copyright: © 2023 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
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