Bibliographic Details
| 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 |
