Ratul Ishrak Jahan, Nishat Mirza Muntasir, Faisal Fahim, Sultana Sadia, Ahmed Ashik, Al Mamun Md Abdullah
Department of EEE, Islamic University of Technology, Gazipur, Bangladesh.
Department of TVE, Islamic University of Technology, Gazipur, Bangladesh.
Heliyon. 2023 Jun;9(6):e17307. doi: 10.1016/j.heliyon.2023.e17307. Epub 2023 Jun 15.
The COVID-19 pandemic has worsened the psychological and social stress levels of university students due to physical illness, enhanced dependence on mobile devices and internet, a lack of social activities, and home confinement. Therefore, early stress detection is crucial for their successful academic performance and mental well-being. The advent of machine learning (ML)-based prediction models can have a crucial impact in predicting stress at its early stages and taking necessary steps for the well-being of individuals. This study aims to develop a reliable machine learning-based prediction model for perceived stress prediction and validate the model using real-world data collected through an online survey among 444 university students from different ethnicity. The machine learning models were built using supervised machine learning algorithms. Principal Component Analysis (PCA) and the chi-squared test were employed as feature reduction techniques. Moreover, Grid Search Cross-Validation (GSCV) and Genetic Algorithm (GA) were employed for hyperparameter optimization (HPO). According to the findings, around 11.26% of individuals were identified with high levels of social stress. In comparison, approximately 24.10% of people were found to be suffering from extremely high psychological stress, which is quite alarming for students' mental health. Furthermore, the prediction results of the ML models demonstrated the most remarkable accuracy (80.5%), precision (1.000), F1 score (0.890), and recall value (0.826). The Multilayer Perceptron model was shown to have the maximum accuracy when combined with PCA as a feature reduction approach and GSCV for HPO. The convenience sampling technique used in this study only considers self-reported data, which may have biased results and lack generalizability. Future research should consider a large sample of data and focus on tracking long-term impacts with coping strategies and interventions. The results of this study can be used to develop strategies to mitigate adverse effects of the overuse of mobile devices and promote student well-being during pandemics and other stressful situations.
新冠疫情因身体疾病、对移动设备和互联网的依赖性增强、缺乏社交活动以及居家隔离等因素，加剧了大学生的心理和社会压力水平。因此，早期压力检测对于他们学业的成功以及心理健康至关重要。基于机器学习（ML）的预测模型的出现，对于在早期阶段预测压力并采取必要措施促进个人幸福安康可能会产生至关重要的影响。本研究旨在开发一个可靠的基于机器学习的感知压力预测模型，并使用通过对来自不同种族的444名大学生进行在线调查收集的真实世界数据对该模型进行验证。机器学习模型是使用监督机器学习算法构建的。主成分分析（PCA）和卡方检验被用作特征约简技术。此外，网格搜索交叉验证（GSCV）和遗传算法（GA）被用于超参数优化（HPO）。根据研究结果，约11.26%的个体被确定为具有高水平的社会压力。相比之下，约24.10%的人被发现患有极高的心理压力，这对学生的心理健康相当令人担忧。此外，ML模型的预测结果显示出最显著的准确率（80.5%）、精确率（1.000）、F1分数（0.890）和召回值（0.826）。当多层感知器模型与作为特征约简方法的PCA以及用于HPO的GSCV相结合时，显示出最高的准确率。本研究中使用的便利抽样技术仅考虑自我报告的数据，这可能会导致结果有偏差且缺乏普遍性。未来的研究应考虑大量的数据样本，并专注于跟踪应对策略和干预措施的长期影响。本研究结果可用于制定策略，以减轻过度使用移动设备的不利影响，并在疫情及其他压力情况下促进学生的幸福安康。
