An enhanced explainable thyroid disease diagnosis by leveraging cluster-smote and machine learning models

An enhanced explainable thyroid disease diagnosis by leveraging cluster-smote and machine learning models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An enhanced explainable thyroid disease diagnosis by leveraging cluster-smote and machine learning models Usman Suleh, Badamasi Alhaji Ahmed, Farouk Lawan Gambo, Fatima Umar Zambuk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8474584/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Thyroid disorders represent a major public health concern worldwide, affecting metabolic regulation and increasing the risk of cardiovascular and systemic complications when not detected early. Existing machine learning (ML) approaches for thyroid disease prediction are often limited by severe class imbalance, suboptimal calibration, and a lack of model interpretability. This study integrates Cluster-based Synthetic Minority Oversampling Technique (Cluster-SMOTE) to preserve minority class structure, alongside multiple machine learning models. The Random Forest classifier emerged as the best-performing model based on the F1-score criterion. Model reliability was further assessed using calibration analysis, Brier score evaluation, and Decision Curve Analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to provide both global and local explanations of model predictions. Experimental evaluation on a publicly available thyroid disease dataset demonstrated that the proposed Random Forest–based framework achieved an F1-score of 0.99, accuracy of 0.99, precision of 0.99, recall of 0.99, AUC of 0.99, and a Brier score of 0.003. DCA further confirmed that the proposed model yields higher net clinical benefit across a wide range of threshold probabilities. These findings demonstrate that combining Cluster-SMOTE, a robust Random Forest classifier, and XAI validation produces an accurate, well-calibrated, and clinically interpretable thyroid disease prediction framework. Thyroid Disease Prediction Explainable Artificial Intelligence Cluster-SMOTE Model Calibration SHAP Clinical Decision Support Imbalanced Data Decision Curve Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers invited by journal 23 Jan, 2026 Editor assigned by journal 31 Dec, 2025 Submission checks completed at journal 30 Dec, 2025 First submitted to journal 29 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8474584","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580723500,"identity":"4d11fb7a-281d-43a1-a8c5-800a98530fe9","order_by":0,"name":"Usman Suleh","email":"","orcid":"","institution":"Abubakar Tafawa Balewa University","correspondingAuthor":false,"prefix":"","firstName":"Usman","middleName":"","lastName":"Suleh","suffix":""},{"id":580723501,"identity":"33d0fa30-1f35-416d-9fad-e0605cefe856","order_by":1,"name":"Badamasi Alhaji Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACAzBiYOBhYGA+8IGBDcg8QLwWtsQZJGkB6TIkTos5++GNn27U1MkwsJ/52MxTxiDHdyOB7eEXPFose9KKpXOOHeZh4Mnd2MxzjsFY8kYCu7EMPocdyDGQzmE7APRL7vbHvG0MiRuAtkhL4NNy/o3x75x/dTwM/G8eNgO11BPWciPHTDq3jZmHQSKHEaQlwQCoRfIDPr/MeFZmndt3mIdN4plh45xzEoYzzzxsk8ajg8GcP3nz7Zxvdfb8/MkPG96U2cjzHU8+JvkDnx4YYINQIE8wNgDdSSpgJMqWUTAKRsEoGCkAAMVOSs0P/VzAAAAAAElFTkSuQmCC","orcid":"","institution":"Federal University Dutse","correspondingAuthor":true,"prefix":"","firstName":"Badamasi","middleName":"Alhaji","lastName":"Ahmed","suffix":""},{"id":580723502,"identity":"10361e97-2b19-4e43-afbf-2388bf3953fa","order_by":2,"name":"Farouk Lawan Gambo","email":"","orcid":"","institution":"Federal University Dutse","correspondingAuthor":false,"prefix":"","firstName":"Farouk","middleName":"Lawan","lastName":"Gambo","suffix":""},{"id":580723503,"identity":"44313101-67f3-4509-8fe4-a2de09eba5e9","order_by":3,"name":"Fatima Umar Zambuk","email":"","orcid":"","institution":"Abubakar Tafawa Balewa University","correspondingAuthor":false,"prefix":"","firstName":"Fatima","middleName":"Umar","lastName":"Zambuk","suffix":""}],"badges":[],"createdAt":"2025-12-29 16:10:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8474584/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8474584/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101297926,"identity":"9799beb1-40e3-4e8f-9b7e-7aab0c71e1e5","added_by":"auto","created_at":"2026-01-28 09:29:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":835620,"visible":true,"origin":"","legend":"","description":"","filename":"Anenhancedexplainablethyroiddiseasediagnosis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8474584/v1_covered_b39929bc-3a39-4a20-a41b-a086af6909c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An enhanced explainable thyroid disease diagnosis by leveraging cluster-smote and machine learning models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humancentric-intelligent-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Human-Centric Intelligent Systems](https://link.springer.com/journal/44230)","snPcode":"44230","submissionUrl":"https://submission.springernature.com/new-submission/44230/3","title":"Human-Centric Intelligent Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thyroid Disease Prediction, Explainable Artificial Intelligence, Cluster-SMOTE, Model Calibration, SHAP, Clinical Decision Support, Imbalanced Data, Decision Curve Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8474584/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8474584/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThyroid disorders represent a major public health concern worldwide, affecting metabolic regulation and increasing the risk of cardiovascular and systemic complications when not detected early. Existing machine learning (ML) approaches for thyroid disease prediction are often limited by severe class imbalance, suboptimal calibration, and a lack of model interpretability. This study integrates Cluster-based Synthetic Minority Oversampling Technique (Cluster-SMOTE) to preserve minority class structure, alongside multiple machine learning models. The Random Forest classifier emerged as the best-performing model based on the F1-score criterion. Model reliability was further assessed using calibration analysis, Brier score evaluation, and Decision Curve Analysis (DCA). SHapley Additive exPlanations (SHAP) were employed to provide both global and local explanations of model predictions. Experimental evaluation on a publicly available thyroid disease dataset demonstrated that the proposed Random Forest\u0026ndash;based framework achieved an F1-score of 0.99, accuracy of 0.99, precision of 0.99, recall of 0.99, AUC of 0.99, and a Brier score of 0.003. 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