Comparative Analysis of Machine Learning Models for Predicting Student Stress Levels: A Multi-Algorithm Approach

ABSTRACT

Student stress has emerged as a critical health concern in academic institutions, with significant implications for academic performance, mental health, and overall well-being. This study compares seven machine learning and statistical modeling approaches to identify determinants of student stress and establish optimal predictive models. Using data from 520 students, we employed linear regression, Random Forest, XGBoost, Support Vector Machines, k-Nearest Neighbors, artificial neural networks, and decision tree algorithms to model stress levels as a function of five key variables: sleep quality, headache frequency, academic performance, study load, and extra-curricular activities. Results demonstrate substantial superiority of non-linear models, with k-NN and XGBoost reducing prediction error by 71-75% compared to linear regression. Study load emerged as the dominant stress determinant (β = 0.3833, p < 2×10⁻¹⁶), accounting for 30.15% of predictive gain in XGBoost models. However, only 17.79% of stress variance was explained by these five variables, indicating multifactorial etiology requiring integration of psychological and environmental factors. We recommend prioritization of study load reduction and implementation of k-NN or XGBoost models for early identification of at-risk students. These findings have significant implications for institutional policy development and student mental health intervention strategies.

Keywords: Student stress, Machine learning, Predictive modeling, Linear regression, XGBoost, k-NN, Academic burden, Mental health