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Related Experiment Video

Updated: Mar 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Reliable Multi-Class Mental Health Prediction Using a WiSARD Discriminator Model on Imbalanced Data.

Muhammad Binsawad¹

¹Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Inquiry : a Journal of Medical Care Organization, Provision and Financing

|March 4, 2026

Summary

This study introduces the WiSARD classifier for accurate multi-class mental disorder prediction, outperforming other models in identifying conditions like depression and anxiety, even with imbalanced data.

Keywords:

RAM-based learning WiSARD classifier clinical decision support imbalanced data machine learning mental disorder prediction multi-class classification psychological diagnosis

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Last Updated: Mar 6, 2026

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Area of Science:

Computational psychiatry
Machine learning for healthcare

Background:

Machine learning (ML) is crucial for mental disorder prediction, aiding early screening and personalized care.
Challenges include high dimensionality, class imbalance, and subtle psychological features in multi-class classification.

Purpose of the Study:

To introduce and evaluate an interpretable, RAM-based WiSARD classifier for multi-disorder mental health prediction.
To compare WiSARD's performance against established ML models on a public dataset.

Main Methods:

A retrospective study used the Kaggle Mental Disorders Dataset (637 complete cases, 29 features).
WiSARD was tested using 10-fold stratified cross-validation against Multilayer Perceptron, Naïve Bayes, DTNB, IB1, and A1DE.
Performance metrics included precision, recall, F-measure, accuracy, MCC, MAE, and KS.

Main Results:

WiSARD achieved superior performance with 98.27% accuracy, 0.983 F-measure, 0.982 MCC, and 0.981 KS.
WiSARD demonstrated better tolerance for misclassifications in minority classes, addressing data imbalance.
An ablation study confirmed WiSARD's reliability and interpretability via RAM-based pattern recognition.

Conclusions:

WiSARD is a promising, interpretable model for multi-class mental disorder prediction, particularly in imbalanced datasets.
Findings are limited to a single non-clinical dataset with self-reported data and require formal psychiatric validation.