DepressNet: an interpretable machine learning framework for depression severity stratification using psychometric data

Abstract

Depression is a highly prevalent mental health condition worldwide, affecting approximately 332 million individuals and contributing significantly to global disability burden. In computational mental health research, machine learning approaches are increasingly applied to structured psychometric questionnaires; however, many existing studies formulate the problem as binary classification, which fails to capture the ordinal nature of symptom severity represented in standardized instruments. This study presents a computational psychometrics framework for depression severity stratification using the Depression Anxiety Stress Scales (DASS-42) questionnaire dataset. The proposed model, DepressNet, four base learners—XGBoost, Random Forest, LightGBM and a calibrated support vector machine (SVM)—with a PyTorch-based multi-layer perceptron meta-learner trained on out-of-fold probability features to reduce information leakage and improve generalization. To address class imbalance inherent in real-world psychometric data, the training pipeline incorporates SMOTETomek resampling together with class-weighted cross-entropy loss. The framework is evaluated on 39,775 real-world questionnaire responses under an imbalanced five-class classification setting using multiple complementary metrics. On a held-out test set, the model achieves 85.38% accuracy, 59.01% macro F1-score, 0.7158 Matthews Correlation Coefficient, and 0.9714 AUC-ROC. It is acknowledged that the severity labels are derived from the same DASS-42 items used as input features; consequently, the framework learns structured scoring patterns within psychometric data rather than predicting independent clinical ground truth. The Mild severity class remains the most challenging stratum (F1 = 0.10), reflecting inherent symptom overlap with adjacent classes and the persistent difficulty of classifying a narrow four-point severity band. Beyond predictive evaluation, a SHAP-based explainability module is applied to examine both global and class-wise feature attribution patterns across severity levels. The analysis provides interpretable relationships between questionnaire items and predicted severity categories, highlighting structured symptom-response patterns captured by the model. Overall, this work contributes a reproducible and interpretable machine learning framework for psychometric data analysis, with emphasis on severity stratification, ensemble-based learning, and explainable modeling of mental health questionnaire responses.