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This study investigates whether machine learning can deliver a more flexible, efficient, and accurate DISC classification. Using data from over 1,000 participants, supervised models and clustering methods were compared, with Logistic Regression achieving 93.53% accuracy and strong cross-validation stability.","Journal of Artificial  \nIntelligence & Robotics Article type: Research Article  \nOpen Access  \nReinventing DISC personality assessment: Machine learning approaches for deeper insights and greater efficiency  \nFatima Kalabi1; Mohammad Hossein Amirhosseini2 *  \n1Queen’s Hospital, Havering and Redbridge University Hospitals NHS Trust, London, United Kingdom.  \n2Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, London, United Kingdom.  \n*Corresponding Author: Mohammad Hossein Amirhosseini  \nDepartment of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, London, United Kingdom.  \nReceived: Jan 20, 2026  \nAccepted: Feb 11, 2026  \nPublished Online: Feb 18, 2026  \n[Website:](Website: www.joaiar.org)[ www.joaiar.org](Website: www.joaiar.org)  \nLicense: © Amirhosseini MH (2026) . This Article is distributed under the terms of Creative Commons Attribution 4.0 International License  \n[Email: m.h.amirhosseini@uel.ac.uk](Email: m.h.amirhosseini@uel.ac.uk) Volume 3 [2026] Issue 1  \nAbstract  \nThe DISC personality framework, while widely adopted in applied settings, relies on a fixed rulebased classification method that may oversimplify individual behavioural profiles. This study explores whether machine learning can offer a more flexible, efficient, and accurate approach to DISC classification. Using a dataset of over 1,000 participants, we evaluated multiple supervised models—including Logistic Regression, XGBoost, SVM, MLP, Random Forest, and K-Nearest Neighbours—alongside unsupervised clustering techniques. Logistic Regression emerged as the top-performing model, achieving 93.53% accuracy and demonstrating superior cross-validation stability.  \nRecursive Feature Elimination identified a reduced set of ten key questionnaire items, maintaining over 91% accuracy and enabling the development of a concise assessment tool. Such a shortened questionnaire offers substantial practical benefits for real-world applications, particularly in fastpaced organisational contexts like recruitment, leadership coaching, and team composition, where rapid yet reliable personality insights are invaluable.  \nClustering analysis further revealed alignment with traditional DISC categories, while uncovering potential hybrid profiles. A comparative clustering analysis between the full 40-item and reduced 10-item questionnaires confirmed that the same behavioural trait structures could be recovered using fewer items. Despite minor differences in cluster alignment, DISC trait patterns remained consistent across both models. These findings confirm that machine learning can replicate and enhance conventional DISC assessments, not only in terms of classification accuracy but also by preserving the conceptual integrity of the DISC framework.  \nThe study validates that the reduced DISC assessment captures the latent personality structure of the original model, offering a scalable and empirically grounded solution for modern psychological evaluation. The complete modelling pipeline, including feature selection and clustering insights, contributes to the growing field of data-driven psychometrics.  \nKeywords: DISC personality assessment; Machine learning; Feature selection; Clustering analysis; Psychometrics; Computational psychometrics; Short-form assessment; Data-driven modelling; Personality classification; Questionnaire optimisation.  \n Amirhosseini MH   \nCitation: Kalabi F, Amirhosseini MH. Reinventing DISC personality assessment: Machine learning approaches for deeper insights and greater efficiency. J Artif Intell Robot. 2026; 3(1): 1037.  \nIntroduction  \nPersonality assessment frameworks play a pivotal role across diverse domains, including leadership development, talent acquisition, team optimisation, and career counselling. Among these, the DISC model—categorising individuals into four behavioural archetypes: Do","cbCaicTQsUV1GnxH","https://ap.wps.com/l/cbCaicTQsUV1GnxH","pdf",1602875,10,1,13,"English","en",105,"# Abstract\n# Introduction\n## Limitations of traditional DISC scoring\n## Motivation for machine learning enhancement\n# Methods (implied from study design)\n## Supervised models and clustering approaches\n## Feature selection and reduced item set\n# Results (implied from abstract)\n## Model performance and stability\n## Short-form questionnaire evaluation\n## Cluster alignment and hybrid profiles\n# Conclusions (implied from abstract)\n## Replication and integrity of DISC framework\n## Scalable, data-driven psychometrics","[{\"question\":\"Why can the traditional DISC assessment misrepresent some individuals?\",\"answer\":\"Traditional scoring assigns a category based on aggregated responses and the highest-scoring dimension, assuming linearity and rigid boundaries. This can oversimplify balanced or hybrid behavioural profiles and lead to potential misclassification.\"},{\"question\":\"Which machine learning model performed best for DISC classification?\",\"answer\":\"Logistic Regression achieved the highest performance, reaching 93.53% accuracy and showing superior cross-validation stability compared with other supervised models.\"},{\"question\":\"How can DISC be made more efficient without losing accuracy?\",\"answer\":\"Recursive Feature Elimination reduced the questionnaire to ten key items while maintaining over 91% accuracy. The study reports that the same underlying behavioural trait structures could be recovered with the shortened questionnaire.\"}]","Reinventing DISC personality assessment - Machine learning approaches for deeper insights and greater efficiency | PDF",1785905188,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"reinventing-disc-personality-assessment-machine-learning-approaches-for-deeper-insights-and-greater-efficiency","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/reinventing-disc-personality-assessment-machine-learning-approaches-for-deeper-insights-and-greater-efficiency/126463/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why can the traditional DISC assessment misrepresent some individuals?","Question",{"text":77,"@type":78},"Traditional scoring assigns a category based on aggregated responses and the highest-scoring dimension, assuming linearity and rigid boundaries. This can oversimplify balanced or hybrid behavioural profiles and lead to potential misclassification.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning model performed best for DISC classification?",{"text":82,"@type":78},"Logistic Regression achieved the highest performance, reaching 93.53% accuracy and showing superior cross-validation stability compared with other supervised models.",{"name":84,"@type":75,"acceptedAnswer":85},"How can DISC be made more efficient without losing accuracy?",{"text":86,"@type":78},"Recursive Feature Elimination reduced the questionnaire to ten key items while maintaining over 91% accuracy. 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