Prediction Modeling of Mental Well-Being Using Health Behavior Data of College Students

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. Due to heightened risks for developing mental illness, this trend is likely to continue during the post-pandemic period. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states. Objective: Studies using machine learning classification of mental well-being are scarce in Asian populations. This investigation aims to develop reliable machine learning classifiers based on health behavior indicators applicable to university students in South-East Asia. Methods: Using data from a large, multi-site cross-sectional survey, this research work models mental well-being and reports on the performance of various machine learning algorithms, such as generalized linear models, k-nearest neighbor, naïve-Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting. Prediction models were evaluated using various metrics such as accuracy, error rate, kappa, sensitivity, specificity, Area Under the recursive operating characteristic Curve (AUC), and Gini Index. Results: Random forest and adaptive boosting algorithms achieved the highest accuracy of identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include body mass index, number of sports activities per week, 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.