| Sumario: | ML-based mental disorder prediction plays an increasingly important role in early screening, clinical decision support, and personalized mental healthcare. However, reliable multi-class classification remains challenging due to high dimensionality, class imbalance, and subtle psychological features. This study describes an interpretable, RAM-based WiSARD classifier for the multi-disorder mental health prediction problem and compares its performance to established models. A retrospective experimental study was carried out in the year 2024 using publicly available mental-health diagnostic data from the Kaggle Mental Disorders Dataset. The dataset consisted of 637 records and 29 symptom-based features representing disorders such as Major Depressive Disorder, Anxiety, PTSD, OCD, ADHD, Bipolar Disorder, and others. Records with missing values, incomplete diagnostic labels, or duplicated entries were excluded. Thus, 637 complete cases were selected for analysis. No clinical identifiers were involved, and hence, ethical clearance was not required. In the present study, WiSARD was tested using a 10-fold stratified cross-validation design against Multilayer Perceptron, Naïve Bayes, DTNB, IB1, and A1DE. The performance was computed using precision, recall, F-measure, accuracy, MCC, MAE, and KS. The study was geographically conducted in Pakistan as part of computational healthcare research. WiSARD classifier achieved the best overall performance with an overall accuracy of 98.27%, F-measure of 0.983, MCC of 0.982, and KS of 0.981, outperforming all baseline models under the same evaluation conditions. Analysis of ROC-AUC, TPR, TNR, and error distributions further showed that WiSARD was more tolerant of misclassifications associated with minority disorder classes, thereby addressing the imbalance present in the dataset. The ablation study verified its contribution to improved reliability and interpretability through RAM-based pattern recognition. The results have shown that WiSARD is a promising, interpretable model for multi-class mental disorder prediction in data-imbalanced settings. At the same time, results are limited to a single non-clinical Kaggle dataset with self-reported observations and without formal psychiatric validation. For this reason, the findings should be interpreted as indicative rather than definitive.
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