DepressNet: An Interpretable Machine Learning Framework for Stratifying Depression Severity From Psychometric Data

Depression affects roughly 332 million people worldwide and stands among the leading causes of disability-adjusted life years, yet most machine learning studies still reduce its diagnosis to a binary question: depressed or not depressed. A new study published in Frontiers in Computer Science argues that this simplification discards exactly the information clinicians care about most — how severe the illness is. Researchers led by Anika Rahman of Stamford University Bangladesh, working with collaborators at Multimedia University in Malaysia, BRAC University, and Woosong University in South Korea, have built a framework called DepressNet that classifies depression severity into five clinically meaningful levels and, crucially, explains which questionnaire items drive each prediction.

The team trained and evaluated DepressNet on 39,775 real-world responses to the Depression Anxiety Stress Scales, or DASS-42, a widely validated self-report instrument collected online through the OpenPsychometrics platform between 2017 and 2019. Each respondent answered 42 items on a four-point Likert scale covering the preceding week’s affective symptoms, alongside demographic questions. Rather than feeding the model precomputed subscale totals — a shortcut that would let the target label leak into the inputs — the researchers used only the raw item-level responses plus eleven demographic variables, for a total of 53 predictors. The severity labels themselves were derived from the standard DASS-42 clinical cutpoints, producing five classes: Normal (10.9 percent of responses), Mild (4.4 percent), Moderate (9.3 percent), Severe (7.2 percent), and Extremely severe, which dominated the dataset at 68.2 percent.

That skew is not a statistical nuisance; it is the central modeling challenge. Online psychometric platforms attract self-selected respondents, and the resulting imbalance means a naive classifier can score highly simply by always predicting the majority class. The researchers countered this with a two-pronged strategy: SMOTETomek resampling, which synthesizes new minority-class examples through SMOTE interpolation while using Tomek links to remove ambiguous borderline points from the majority class, combined with a class-weighted cross-entropy loss that penalizes errors on rare severity levels more heavily. Resampling was applied only to the training partition, expanding it to 108,535 balanced instances, while the 7,955-sample held-out test set retained its natural, imbalanced distribution to reflect real deployment conditions.

Architecturally, DepressNet is a two-level stacking ensemble. Four heterogeneous base learners — XGBoost, Random Forest, LightGBM, and a calibrated support vector machine — each produce five-class probability vectors. These probabilities are generated through five-fold stratified cross-validation using an out-of-fold protocol: each base learner is trained on four folds and predicts the held-out fifth, ensuring the meta-learner never sees predictions on data its base models were trained on. The resulting meta-feature matrix, with 20 probability columns per sample, feeds a PyTorch multi-layer perceptron with two hidden layers of 256 and 128 units, ReLU activations, batch normalization, and dropout regularization, trained on a Tesla T4 GPU with cosine annealing learning-rate scheduling over 60 epochs.

On the held-out test set, DepressNet achieved 85.38 percent accuracy, a macro F1-score of 59.01 percent, a Matthews Correlation Coefficient of 0.7158, and an AUC-ROC of 0.9714 — the best accuracy, macro F1, and MCC among seven models evaluated, edging out a standalone MLP baseline that reached 85.02 percent accuracy and 0.7095 MCC. The macro F1 figure, which weights all classes equally regardless of size, tells a more sobering story than the headline accuracy: performance varied enormously across severity strata. The Extremely severe class, with its distinctive symptom profile and overwhelming representation, scored a perfect F1 of 1.00. The Severe class showed high recall of 0.93, indicating the model prioritized sensitivity for clinically urgent cases. But the Mild class collapsed to an F1 of just 0.10 — a result the authors traced to structural, not algorithmic, causes.

The Mild stratum occupies a narrow four-point band on the scaled DASS-42 depression score (10 to 13), squeezed between Normal (0 to 9) and Moderate (14 to 20). Its symptom patterns overlap substantially with both neighbors, creating inherent boundary ambiguity that no amount of resampling or cost-sensitive learning could resolve. When the team retrained the meta-learner with Focal Loss, a technique designed to emphasize hard-to-classify minority examples, the Mild F1 remained stuck at 0.10 while overall macro F1 actually dipped slightly. The authors suggest that ordinal classification methods, hierarchical two-stage classification, and boundary-aware oversampling targeted specifically at the Mild stratum may fare better in future work.

Statistical validation reinforced the picture of consistent but modest gains. The Friedman test across five-fold cross-validation macro F1-scores found significant differences among all seven models (p = 0.0007), and the Nemenyi post-hoc test confirmed a significant pairwise advantage for DepressNet over the standalone MLP (p = 0.000343). However, the McNemar test on per-sample test-set predictions did not reach significance (p = 0.170), and the authors are candid about this: the improvements should be read as consistent empirical gains under the adopted protocol rather than statistically demonstrated superiority at the level of individual predictions. An ablation study further showed that removing Random Forest from the ensemble caused the largest performance drop, confirming the value of combining bagging-based and gradient-boosting learners with complementary decision boundaries.

The most distinctive contribution may be the framework’s dual explainability module. Applying SHAP (SHapley Additive exPlanations) to the trained XGBoost base learner, the researchers computed feature attributions both globally and separately for each severity class. The global rankings aligned precisely with the core symptom domains of major depressive disorder as defined in the DSM-5: the top item was “I felt sad and depressed,” followed by “I felt I wasn’t worth much as a person,” “I felt I was pretty worthless,” and “I felt down-hearted and blue.” The class-wise decomposition revealed a clinically coherent progression pathway — transient everyday low affect in the Normal class, emerging worthlessness in Mild, self-schema disruption and anhedonia onset in Moderate, entrenched negative self-belief and hopelessness in Severe, and existential collapse with motivational disintegration in Extremely severe. To the authors’ knowledge, this is the first study to apply class-wise SHAP decomposition across all five DASS-42 severity levels.

The researchers are equally forthright about the study’s central methodological caveat. Because the severity labels are derived from the same DASS-42 items used as input features, the framework learns structured scoring patterns within the questionnaire rather than predicting independent clinical ground truth. The SHAP attributions, while clinically plausible, reflect model-learned associations and should not be interpreted as causal mechanisms. The dataset, though large and demographically diverse, consists entirely of voluntary online self-reports with no clinician assessment, and the model has not been evaluated on hospital-based cohorts or independently diagnosed samples. The authors position DepressNet as a reproducible computational framework for psychometric analysis — not a screening or diagnostic tool — and call for validation on clinically labeled cohorts as a necessary condition before any real-world deployment.

Those caveats aside, the work signals where computational mental health research is heading: away from crude binary thresholds and toward severity-aware, interpretable models that can show their reasoning. In a field where over 75 percent of affected individuals in low- and middle-income countries receive no adequate treatment, tools that can stratify risk transparently — and admit precisely where they fail, as DepressNet does with its Mild class — may prove as valuable for building clinical trust as for raw predictive power.

Subject of Research: Machine learning stratification of depression severity from DASS-42 psychometric questionnaire data using an interpretable stacking ensemble with SHAP explainability

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

Article References: Rahman, A., Al Farid, F., Mehedi, M. H. K., Uddin, J., & Abdul Karim, H. B. (2026). DepressNet: an interpretable machine learning framework for depression severity stratification using psychometric data. Frontiers in Computer Science, 8, Article 1901490. https://doi.org/10.3389/fcomp.2026.1901490

Image Credits: AI Generated

DOI: 10.3389/fcomp.2026.1901490

Keywords: DepressNet, depression severity, DASS-42, machine learning, stacking ensemble, SHAP, explainable AI, class imbalance, SMOTETomek, computational psychometrics, mental health informatics, XGBoost