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…
