An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

Tran Anh Tuan; Le Thanh Thao Trang; Tran Dai An; Nguyen Huu Nghia; Dao Thi Thanh Loan

doi:10.28991/HEF-2024-05-01-01

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Tran Anh Tuan
tattinsphue@yahoo.com
1) School of Informatics, Walailak University, Nakhon Si Thammarat, 80160, Thailand. 2) Informatics Innovation Center of Excellence (IICE), Walailak University, Nakhon Si Thammarat, 80160,, Thailand https://orcid.org/0000-0002-0920-4726
Le Thanh Thao Trang Faculty of Basic Science and Public Health, Dong Thap Medical College, Dong Thap, 81000,, Viet Nam
Tran Dai An Dong Thap Provincial Center for Disease Control, Dong Thap, 81000,, Viet Nam
Nguyen Huu Nghia Quang Binh Medial College, Quang Binh, 47100,, Viet Nam
Dao Thi Thanh Loan Faculty of Foundational Sciences, Dak Lak College of Pedagogy, Dak Lak, 63000,, Viet Nam

Vol. 5 No. 1 (2024): March

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Objectives: This study explores different machine learning models (KNN: k-nearest neighbor, MLP: Multilayer Perceptron, SVM: Support Vector Machine) to identify the optimal model for accurate and rapid mental health detection among the recovered COVID-19 patients. Other techniques are also investigated, such as feature selection (Recursive Feature Elimination (RFE) and Extra Trees (ET) methods) and hyper-parameter tuning, to achieve a system that could effectively and quickly indicate mental health. Method/Analysis: To achieve the objectives, the study employs a dataset collected from recovered COVID-19 patients, encompassing information related to depression, anxiety, and stress. Machine learning models are utilized in the analysis. Additionally, feature selection methods and hyper-parameter tuning techniques are explored to enhance the model's predictive capabilities. The performance of each model is assessed based on accuracy metrics. Findings: The experimental results show that SVM is the most suitable model for accurately predicting an individual's mental health among recovered COVID-19 patients (accuracy â‰¥ 0.984). Furthermore, the ET method is more effective than the RFE method for feature selection in the anxiety and stress datasets. Novelty/Improvement:The study lies in the understanding of predictive modeling for mental health and provides insights into the choice of models and techniques for accurate and early detection.

Doi: 10.28991/HEF-2024-05-01-01

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Tuan, T. A., Trang, L. T. T., An, T. D., Nghia, N. H., & Loan, D. T. T. (2024). An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients. Journal of Human, Earth, and Future, 5(1), 1–18. https://doi.org/10.28991/HEF-2024-05-01-01

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An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

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An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

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