Secure and Weighted Hybrid Machine Learning Framework for Accurate Academic Stress Monitoring

Stress among college students has increasingly become a serious concern, affecting both academic performance and overall well-being. This study presents the development and implementation of a privacy- aware, hybrid machine learning system designed to monitor and manage student stress levels in a college environment. An Android application was developed to collect real-time data on students’ physiological and behavioural indicators, including Heart Rate, Sleep Hours, Academic Load, Screen Time, Physical Activity, and Mood Score. The collected data were analysed using a Hybrid Random Forest–SVM model, where feature importance weighting and adaptive probability fusion were applied to enhance predictive accuracy. Additionally, AES encryption was integrated to ensure the secure handling of sensitive student information. Based on the assessed stress levels, the system provides tailored guidance and supportive strategies aimed at reducing psychological strain and promoting mental wellness. The proposed approach SWH Framework allows continuous monitoring and early identification of high stress levels, enabling timely intervention. Experimental results demonstrate that the system achieves 92% accuracy, outperforming existing single-model approaches. These findings suggest that hybrid, privacy-aware mobile health technologies can serve as effective, reliable, and accessible tools for supporting stress management among college students and enhancing their overall academic experience