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Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students

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Title: Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students
Authors: Hanif Abdul Rahman, Madeline Kwicklis, Mohammad Ottom, Areekul Amornsriwatanakul, Khadizah H. Abdul-Mumin, Michael Rosenberg, Ivo D. Dinov
Source: Bioengineering, Vol 10, Iss 5, p 575 (2023)
Publisher Information: MDPI AG, 2023.
Publication Year: 2023
Collection: LCC:Technology
LCC:Biology (General)
Subject Terms: mental well-being, machine learning, algorithms, university students, Asian population, health behaviors, Technology, Biology (General), QH301-705.5
More Details: 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2306-5354
Relation: https://www.mdpi.com/2306-5354/10/5/575; https://doaj.org/toc/2306-5354
DOI: 10.3390/bioengineering10050575
Access URL: https://doaj.org/article/8df8056852c24b50aad1574b3c55d2d2
Accession Number: edsdoj.8df8056852c24b50aad1574b3c55d2d2
Database: Directory of Open Access Journals
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  Data: Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students
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  Data: <searchLink fieldCode="AR" term="%22Hanif+Abdul+Rahman%22">Hanif Abdul Rahman</searchLink><br /><searchLink fieldCode="AR" term="%22Madeline+Kwicklis%22">Madeline Kwicklis</searchLink><br /><searchLink fieldCode="AR" term="%22Mohammad+Ottom%22">Mohammad Ottom</searchLink><br /><searchLink fieldCode="AR" term="%22Areekul+Amornsriwatanakul%22">Areekul Amornsriwatanakul</searchLink><br /><searchLink fieldCode="AR" term="%22Khadizah+H%2E+Abdul-Mumin%22">Khadizah H. Abdul-Mumin</searchLink><br /><searchLink fieldCode="AR" term="%22Michael+Rosenberg%22">Michael Rosenberg</searchLink><br /><searchLink fieldCode="AR" term="%22Ivo+D%2E+Dinov%22">Ivo D. Dinov</searchLink>
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  Data: Bioengineering, Vol 10, Iss 5, p 575 (2023)
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  Data: MDPI AG, 2023.
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  Data: <searchLink fieldCode="DE" term="%22mental+well-being%22">mental well-being</searchLink><br /><searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22algorithms%22">algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22university+students%22">university students</searchLink><br /><searchLink fieldCode="DE" term="%22Asian+population%22">Asian population</searchLink><br /><searchLink fieldCode="DE" term="%22health+behaviors%22">health behaviors</searchLink><br /><searchLink fieldCode="DE" term="%22Technology%22">Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Biology+%28General%29%22">Biology (General)</searchLink><br /><searchLink fieldCode="DE" term="%22QH301-705%2E5%22">QH301-705.5</searchLink>
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  Data: 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.
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