Predictions of College Students’ Mental Stress using Machine Learning Algorithms
Keywords:
Stress monitoring systems, Early intervention, Academic performance, Physiological data, Real-time stress prediction, Stress managementAbstract
The mental health of college students has become an increasingly urgent concern, given the rising levels of academic stress and its detrimental effects on well-being and performance. Traditional methods for identifying and managing student stress—such as surveys, questionnaires, interviews, and observational techniques—are often reactive and lack the capacity for timely intervention. Consequently, there is a growing need for predictive, data-driven systems that can offer real-time insights into stress levels. In this research, we present a machine learning–based framework designed to predict mental stress among college students.
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