Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students
Issued Date
2023-05-01
Resource Type
eISSN
23065354
Scopus ID
2-s2.0-85160688667
Journal Title
Bioengineering
Volume
10
Issue
5
Rights Holder(s)
SCOPUS
Bibliographic Citation
Bioengineering Vol.10 No.5 (2023)
Suggested Citation
Abdul Rahman H., Kwicklis M., Ottom M., Amornsriwatanakul A., H. Abdul-Mumin K., Rosenberg M., Dinov I.D. Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students. Bioengineering Vol.10 No.5 (2023). doi:10.3390/bioengineering10050575 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/82984
Title
Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students
Other Contributor(s)
Abstract
Background: Since the onset of the COVID-19 pandemic in early 2020, the importance of timely and effective assessment of mental well-being has increased dramatically. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states. Methods: We used data from a large, multi-site cross-sectional survey consisting of 17 universities in Southeast Asia. This research work models mental well-being and reports on the performance of various machine learning algorithms, including generalized linear models, k-nearest neighbor, naïve Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting. Results: Random Forest and adaptive boosting algorithms achieved the highest accuracy for identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include the number of sports activities per week, body mass index, grade point average (GPA), sedentary hours, and age. Conclusions: Based on the reported results, several specific recommendations and suggested future work are discussed. These findings may be useful to provide cost-effective support and modernize mental well-being assessment and monitoring at the individual and university level.