Reliable Multi-Class Mental Health Prediction Using a WiSARD Discriminator Model on Imbalanced Data.
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....
| Publicado en: | Inquiry (00469580) Vol. 63; pp. 1 - 17 |
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| Formato: | Artículo |
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Sage Publications Inc.
03/04/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=192081232&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 192081232 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 03/04/2026 vid: 63 pid: 344 pub: Sage Publications Inc. artinfo: ui: 192081232 10.1177/00469580261418270 ppf: 1 ppct: 16 formats: tig: atl: Reliable Multi-Class Mental Health Prediction Using a WiSARD Discriminator Model on Imbalanced Data. aug: au: Binsawad, Muhammad affil: Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia su: Psychiatric diagnosis Post-traumatic stress disorder Bipolar disorder Mental health Prediction models Research funding Receiver operating characteristic curves Attention-deficit hyperactivity disorder Mental status examination Mental illness Statistical sampling Research evaluation Clinical decision support systems Retrospective studies Anxiety Diagnostic errors Experimental design Obsessive-compulsive disorder Medical records Acquisition of data Prediction algorithms Comparative studies Machine learning Mental depression Pakistan sug: subj: Pakistan Psychiatric diagnosis Post-traumatic stress disorder Bipolar disorder Mental health Prediction models Research funding Receiver operating characteristic curves Attention-deficit hyperactivity disorder Mental status examination Mental illness Statistical sampling Research evaluation Clinical decision support systems Retrospective studies Anxiety Diagnostic errors Experimental design Obsessive-compulsive disorder Medical records Acquisition of data Prediction algorithms Comparative studies Machine learning Mental depression keyword: clinical decision support imbalanced data machine learning mental disorder prediction multi-class classification psychological diagnosis RAM-based learning WiSARD classifier ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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