Prediction of Mental Stress-handling Capability of Students using Support Vector Machine

This study investigates the impact of mental stress on students' Mental Stress-Handling Capability (MSHC) and its association with changes in body composition and mental activity behaviors during online exams. Over a 13-week semester, the research identifies significant variations in psychological working ability and body composition across different MSHC levels. Remarkably, students with "excellent" MSHC demonstrated increased resilience to heightened mental stress, maintaining stable health and psychological activity. In contrast, those classified as "good" or "average" experienced declines in vigorous mental activity and dietary intake under pressure. Employing a Support Vector Machine (SVM) based machine learning model, the study achieves high accuracy in assessing and predicting students' capacity to manage mental stress. These findings underscore the pivotal role of MSHC in determining individual responses to stress, emphasizing the need for tailored interventions to support students with varying stress-handling capabilities.