A Hybrid Model for Prostate Cancer Detection and Prognosis: A Systematic Review

A Hybrid Model for Prostate Cancer Detection and Prognosis: A Systematic Review | 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 Systematic Review A Hybrid Model for Prostate Cancer Detection and Prognosis: A Systematic Review Uduak Umoh, Udoinyang Inyang, Blessing E. Akponome This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9303019/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 This study systematically reviews the integration of Interval Type-2 Fuzzy Logic (IT2FL) and XGBoost for prostate cancer detection and prognosis. Prostate cancer is a significant health concern, with existing diagnostic methods often challenged by data uncertainties and accuracy limitations. IT2FL effectively addresses uncertainties in medical data, while XGBoost enhances classification performance. The methodology involved a systematic search across databases, including Google Scholar, IEEE Xplore, Elsevier, and Springer, identifying 121 studies from an initial pool of 500 articles. The review investigates four key aspects: the integration of IT2FL and XGBoost, input parameter significance, the role of linguistic variables, and the impact of XGBoost on diagnostic accuracy. Visualization tools such as VosViewer and Matplotlib facilitated data analysis and presentation. Key findings highlight the hybrid model's ability to enhance diagnostic precision, reduce false positives, and support informed clinical decision-making. However, challenges like data diversity and model interpretability persist. The study highlights the potential of the hybrid model in advancing precision medicine by enhancing the detection and prognosis of prostate cancer. This paper demonstrates the feasibility and effectiveness of combining IT2FL and XGBoost, paving the way for future integration of explainable AI techniques and broader clinical applications. Interval Type-2Fuzzy Logic (IT2FL) XGboost Prostate Cancer Detection Hybrid Machine Learning Models Medical Decision Support Systems Full Text Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9303019","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":616587682,"identity":"736ee037-f90b-4eed-8d31-453570d894e4","order_by":0,"name":"Uduak Umoh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFCCBAbGBiBlwN4AYoOBAZFaeA6QrEUiAS6EX4vB8RzDjzPbtsmZS74xe/Cw7bAcA3vzNgnGHbW4tZx5Yyy5se22seXsHHODxLbDxgw8x8okGM8cx63lRo6B5MO224kbbueYSQC1JDZIABmMbcfwaTH+CdZy8wxUi/wbglrMQA5L3HCDB2YLD0hLDU4tkmeelVnOOHfb2OBMWplEwrl0YzaetGKLxLYDOLXwHU/efLOn7LacwfHD2yR/lFnL8bMf3njjY1sdTi3ooJmBDUQlMBwmWksdBmMUjIJRMApGAQBlXVrcjr1gdgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Uyo","correspondingAuthor":true,"prefix":"","firstName":"Uduak","middleName":"","lastName":"Umoh","suffix":""},{"id":616587790,"identity":"7a6fbf89-dd27-4c60-9ed9-7fbd074dbff8","order_by":1,"name":"Udoinyang Inyang","email":"","orcid":"","institution":"University of Uyo","correspondingAuthor":false,"prefix":"","firstName":"Udoinyang","middleName":"","lastName":"Inyang","suffix":""},{"id":616587924,"identity":"68d3899e-5c91-4d29-a937-dfcc23e9d4d0","order_by":2,"name":"Blessing E. Akponome","email":"","orcid":"","institution":"University of Uyo","correspondingAuthor":false,"prefix":"","firstName":"Blessing","middleName":"E.","lastName":"Akponome","suffix":""}],"badges":[],"createdAt":"2026-04-02 12:11:09","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9303019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9303019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106094508,"identity":"c8299f9b-371e-46ba-b1b2-3d34048dc165","added_by":"auto","created_at":"2026-04-03 11:42:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":669572,"visible":true,"origin":"","legend":"","description":"","filename":"AHybridModelforProstateCancerDetectionandPrognosisASystematicReview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9303019/v1_covered_543e767a-4b9e-4436-84cb-be03c279c589.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA Hybrid Model for Prostate Cancer Detection and Prognosis: A Systematic Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Uyo","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":"Interval Type-2Fuzzy Logic (IT2FL), XGboost, Prostate Cancer Detection, Hybrid Machine Learning Models, Medical Decision Support Systems","lastPublishedDoi":"10.21203/rs.3.rs-9303019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9303019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study systematically reviews the integration of Interval Type-2 Fuzzy Logic (IT2FL) and XGBoost for prostate cancer detection and prognosis. Prostate cancer is a significant health concern, with existing diagnostic methods often challenged by data uncertainties and accuracy limitations. IT2FL effectively addresses uncertainties in medical data, while XGBoost enhances classification performance. The methodology involved a systematic search across databases, including Google Scholar, IEEE Xplore, Elsevier, and Springer, identifying 121 studies from an initial pool of 500 articles. The review investigates four key aspects: the integration of IT2FL and XGBoost, input parameter significance, the role of linguistic variables, and the impact of XGBoost on diagnostic accuracy. Visualization tools such as VosViewer and Matplotlib facilitated data analysis and presentation. Key findings highlight the hybrid model's ability to enhance diagnostic precision, reduce false positives, and support informed clinical decision-making. However, challenges like data diversity and model interpretability persist. The study highlights the potential of the hybrid model in advancing precision medicine by enhancing the detection and prognosis of prostate cancer. This paper demonstrates the feasibility and effectiveness of combining IT2FL and XGBoost, paving the way for future integration of explainable AI techniques and broader clinical applications.\u003c/p\u003e","manuscriptTitle":"A Hybrid Model for Prostate Cancer Detection and Prognosis: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 06:47:52","doi":"10.21203/rs.3.rs-9303019/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a21495f3-80e2-467e-8930-790d328a0988","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T06:47:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 06:47:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9303019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9303019","identity":"rs-9303019","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}