Abstract:
Student stress and smartphone addiction have emerged as critical issues in contemporary academic environments, affecting mental health, academic performance, and overall well-being. This study explores the intricate relationships among behavioral factors, physiological indicators, and smartphone usage patterns using machine learning (ML) techniques. A total of ten regression models—Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), ElasticNet, XGBoost, LightGBM, and CatBoost— were evaluated for their ability to predict self-reported stress and addiction levels among students. Performance was measured using MSE, RMSE, R2, and computational efficiency. Results revealed that CatBoost demonstrated superior performance for stress prediction, achieving the lowest MSE (1.634) and highest R2 (0.793), while Linear Regression performed best for addiction prediction with the lowest MSE (0.377) and highest R2 (0.954). Correlation analysis highlighted strong associations between high stress levels and poor academic performance (r = 0.85), reduced sleep duration (r = –0.69), and high smartphone dependency. Notably, nighttime phone usage, frequent device unlocks, and high notification counts were found to significantly influence both stress and addiction levels. Beyond model accuracy, this study provides a comprehensive impact analysis across societal, environmental, ethical, and sustainability dimensions. It emphasizes the urgent need for proactive strategies in educational and mental health domains to mitigate digital overdependence and stress. Furthermore, it advocates for sustainable research practices, including energy-efficient computing and privacy-centered ethical frameworks, aligning technological progress with social responsibility. The findings pave the way for targeted interventions through mobile applications and policy initiatives to enhance student well-being in an increasingly digital academic landscape.
