An Interpretable Hybrid CNN-SVM Model for Predicting Stress Among University Students

Stress among university students can be defined as a state of mental tension caused by difficult academic endeavours. Nonetheless, stress at the tertiary (i.e., University) level has been the subject of much research as accurately predicting mental stress is crucial for promoting student well-being and academic success. In recent research, Machine Learning (ML) and Deep Learning (DL) models have been utilized for determining the academic stress. However, majority of these works utilizes isolated ML or DL models without having an in-depth assessment of the feature interpretation (i.e., the mental stressors) with respect to model performance. In this study, a novel hybrid CNN-SVM model is proposed to predict the mental stress among the university students. The rationale here is that the feature extraction capability of Convolutional Neural Networks (CNN) blended with the classification proficiency of Support Vector Machines (SVM) would better extract the relevant mental stressors towards accurate stress prediction. The model is trained and tested with the student mental stress data as measured using the PSS-10 model from 15 top-ranked universities in Bangladesh. Alongside, Explainable Artificial Intelligence (XAI) is used to validate the model performance. Reported results reveal superior performance of the hybrid CNN-SVM model in relation to conventional ML and DL models, achieving an accuracy of 97.34% with precision 97.38%, recall 97.34%, and f1-score 97.36%. XAI assessment shows that the improvement is due to the models' efficacy in utilizing the essential mental stressors than the conventional models. We argue that the overall contribution can be used for early detection of student mental stress and assist in planning for student counseling, health assessment, and academic process improvement.