Multi-Class Mental Health Classification Based on DASS-21 and Perceived Social Support Using Machine Learning Algorithms
TL;DR
Improvements in recall and F1-score for minority classes demonstrate the effectiveness of the balancing process and highlight the potential of machine learning for student mental health classification, although further validation on larger and more diverse datasets is required.
Abstract
Mental health issues among students require data-driven approaches for early identification. This study aims to classify students’ mental health levels using the Depression Anxiety Stress Scale (DASS-21) and perceived social support, measured by the Multidimensional Scale of Perceived Social Support (MSPSS), via machine learning algorithms. A supervised classification approach was employed using Random Forest, Support Vector Machine, and Logistic Regression on data collected from 450 respondents. The data were processed through scoring, labeling, encoding, balancing, and stratified 80:20 splitting. Model evaluation was conducted using hold-out testing and 5-fold cross-validation to ensure robust and reliable performance estimation. The results indicate that Random Forest achieved the best performance, with an accuracy of 0.97 on the test set, outperforming Support Vector Machine (0.81) and Logistic Regression (0.83). Improvements in recall and F1-score for minority classes demonstrate the effectiveness of the balancing process. These findings highlight the potential of machine learning for student mental health classification, although further validation on larger and more diverse datasets is required.