Abstract
Mental health severity assessment is often hindered by limited access to professional services and the time required for clinical evaluation. This study proposes an interpretable soft voting method to classify the severity levels of depression, anxiety, and stress using DASS-42 questionnaire data. The proposed framework integrates Logistic Regression, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, and is evaluated on 35,445 anonymized responses from a public psychometric dataset. Model performance was assessed using accuracy, precision, recall, F1-score, macro-averaged and weighted F1-score and the precision-recall curve under stratified cross-validation for class imbalance between the normal and mild classes. Explainable Artificial Intelligence using SHAP was employed to interpret model decisions. The soft voting achieved strong predictive performance, with accuracy values of 0.98 for depression, 0.99 for anxiety, and 0.97 for stress, outperforming or matching individual base models. SHAP analysis identified clinically consistent features contributing to model predictions; due to computational constraints, SHAP analysis was not applied to the SVM model. Despite strong performance, the use of secondary self-reported data and class imbalance, particularly the underrepresentation of normal and mild cases, were limitations. The proposed model demonstrates the potential of interpretable soft voting as a decision-support tool for mental health severity stratification in resource-constrained settings.