Identification of the Recurrence of Differentiated Thyroid Cancer by Stacking Classifier

Identification of the Recurrence of Differentiated Thyroid Cancer by Stacking Classifier | 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 Identification of the Recurrence of Differentiated Thyroid Cancer by Stacking Classifier Sulekha Das, Avijit Kumar Chaudhuri, Nobhonil Roy Choudhury, Partha Ghosh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5713674/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The performance of different machine learning models for predicting well-differentiated thyroid cancer recurrence is compared in this study using several accuracy metrics such as accuracy, sensitivity, precision, F1 score, specificity, the area under the curve (ROC), and Kappa statistics. The models that the paper considered for ranking are Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and the proposed Stacked model. The results suggest that the use of ensemble learning methods, especially the proposed Stacked model, results in a generalized improvement over individual classifiers in terms of most of the measures. From Stacked models, there was a boosted level of sensitivity, precision, and F1-score, and the AUC in the higher train-test split (such as 80-20%) and 30-fold cross-validation where the accuracy was at par 100% and consistent. Random Forest also showed good accuracy of results and increased their speed when working with large data sets. The best outcomes were achieved using Decision Trees depending on the 80-20 split and 30-fold cross-validation. However, in Naive Bayes, which was used as a baseline, all the metrics were the lowest, indicating its inapplicability to this data set. Among the ensemble models, the newly designed Stacked model is the best for prediction accuracy of thyroid cancer recurrence; Random Forest is preferred for volume datasets. The results imply that using ensemble methods of constructing classifiers and selecting training data splits are indicative of operationalizing better models in intricate classification problems. Differentiated Thyroid Cancer Machine-Learning Classifiers Stacking Classifier Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-5713674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394771077,"identity":"e1fe6797-b255-4c4a-ba46-078407427f2d","order_by":0,"name":"Sulekha Das","email":"","orcid":"","institution":"GCECT","correspondingAuthor":false,"prefix":"","firstName":"Sulekha","middleName":"","lastName":"Das","suffix":""},{"id":394771078,"identity":"9ea5b5a6-2cc5-40b5-adbe-26294f03a746","order_by":1,"name":"Avijit Kumar Chaudhuri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYLCCBB4JBn4wo4AELRKSDSCGAQkWSRgcAFHEaOFvYH4m8UDGos74/OrEDw8MGOT5xQ4QMP0Am5kEyGFmN95ulgA6zHDm7AT8WoAuMTaAaDm7AaQlweA2QS3sn8FajGec3fyDSC08hg9AWgz4e7cRZ4vEYZ5CkBbJGTd4t1kkGEgQ9gt/e/uGgz976vj5+89uvvmjwkaeX5qAFgZmIGbsAdkHVilBQDkc/ADZd4BY1aNgFIyCUTDSAAB+Izu3BQE37AAAAABJRU5ErkJggg==","orcid":"","institution":"Brainware University","correspondingAuthor":true,"prefix":"","firstName":"Avijit","middleName":"Kumar","lastName":"Chaudhuri","suffix":""},{"id":394771079,"identity":"3b55d721-e2f3-48ca-8661-f0bd94584d07","order_by":2,"name":"Nobhonil Roy Choudhury","email":"","orcid":"","institution":"Brainware University","correspondingAuthor":false,"prefix":"","firstName":"Nobhonil","middleName":"Roy","lastName":"Choudhury","suffix":""},{"id":394771080,"identity":"f5bc1f84-b747-40da-a47b-9d71fd9497fa","order_by":3,"name":"Partha Ghosh","email":"","orcid":"","institution":"GCECT","correspondingAuthor":false,"prefix":"","firstName":"Partha","middleName":"","lastName":"Ghosh","suffix":""}],"badges":[],"createdAt":"2024-12-26 05:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5713674/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5713674/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72866975,"identity":"2dadbed3-9b71-481e-8af7-1dcc1c33f0d2","added_by":"auto","created_at":"2025-01-03 06:14:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":415960,"visible":true,"origin":"","legend":"","description":"","filename":"DTC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5713674/v1_covered_cac02c22-e859-4e17-965b-c87303014636.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of the Recurrence of Differentiated Thyroid Cancer by Stacking Classifier","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Differentiated Thyroid Cancer, Machine-Learning Classifiers, Stacking Classifier","lastPublishedDoi":"10.21203/rs.3.rs-5713674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5713674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe performance of different machine learning models for predicting well-differentiated thyroid cancer recurrence is compared in this study using several accuracy metrics such as accuracy, sensitivity, precision, F1 score, specificity, the area under the curve (ROC), and Kappa statistics. 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