Abdul Rahman Hanif, Kwicklis Madeline, Ottom Mohammad, Amornsriwatanakul Areekul, H Abdul-Mumin Khadizah, Rosenberg Michael, Dinov Ivo D
Statistics Online Computational Resource (SOCR), University of Michigan, Ann Arbor, MI 48109, USA.
PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Gadong BE1410, Brunei.
Bioengineering (Basel). 2023 May 10;10(5):575. doi: 10.3390/bioengineering10050575.
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.
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.
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.
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.
自2020年初新冠疫情爆发以来，及时有效地评估心理健康的重要性急剧增加。机器学习（ML）算法和人工智能（AI）技术可用于早期检测、预后评估以及对负面心理健康状态的预测。
我们使用了来自东南亚17所大学的一项大型多地点横断面调查的数据。这项研究工作对心理健康进行建模，并报告了各种机器学习算法的性能，包括广义线性模型、k近邻算法、朴素贝叶斯算法、神经网络、随机森林、递归划分、装袋法和提升法。
随机森林和自适应提升算法在识别负面心理健康特征方面取得了最高准确率。与预测心理健康不佳相关的最显著的五个特征包括每周体育活动的次数、体重指数、平均绩点（GPA）、久坐时长和年龄。
基于所报告的结果，讨论了若干具体建议和未来工作方向。这些发现可能有助于提供具有成本效益的支持，并使个人和大学层面的心理健康评估与监测现代化。
