Data mining techniques for increasing resolution of refractive index measurements for precise cervical intraepithelial neoplasia and cervical cancer classification

Data mining techniques for increasing resolution of refractive index measurements for precise cervical intraepithelial neoplasia and cervical cancer classification | 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 Article Data mining techniques for increasing resolution of refractive index measurements for precise cervical intraepithelial neoplasia and cervical cancer classification Anna Drabik-Kruczkowska, Michal Kruczkowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6388221/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 Cervical cancer remains a critical global health issue, with early detection and precise classification of cervical intraepithelialneoplasia (CIN) essential for effective management. This study introduces a machine learning-based approach to enhance theresolution of refractive index measurements, a key factor in diagnosing CIN and cervical cancer. We employed supervisedlearning to predict the refractive indices of liquid samples, conducting low-coherence interferometric measurements within therange of 1.3 to 1.5, with increments of 0.01. The optical spectra obtained were transformed into feature vectors tailored for aclassification model, which was then applied to a cervical tissue phantom made from liquids with known refractive indices. Thisallowed us to classify refractive index changes into five distinct categories: normal tissue, CIN-1, CIN-2, CIN-3, and cancerousstates. The model demonstrated high performance, with cross-validation scores showing a classification accuracy of 0.93,precision of 0.94, recall of 0.91, and an F1-score of 0.92 on the training/validation dataset. Testing results were similarly robust,with a classification accuracy of 0.91, precision of 0.90, recall of 0.93, and an F1-score of 0.91. These metrics confirm themodel’s high prognostic accuracy and its potential for application to new data. Additionally, our methodology enabled thegeneration of synthetic data mimicking experimental signals, extending the resolution of the acquired results. This enhancementoffers a promising tool for improving diagnostic methods based on refractive index measurements, potentially advancing earlydetection and classification in cervical cancer screening. Biological sciences/Cancer/Gynaecological cancer Health sciences/Medical research Physical sciences/Mathematics and computing Physical sciences/Optics and photonics 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. 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-6388221","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456419885,"identity":"3a54ac32-4d6a-4807-b1cf-9c6a5636c50e","order_by":0,"name":"Anna Drabik-Kruczkowska","email":"","orcid":"","institution":"Collegium Medicum in Bydgoszcz","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Drabik-Kruczkowska","suffix":""},{"id":456419888,"identity":"40e4974a-80bb-4efc-b5e6-1b32f4abd0ee","order_by":1,"name":"Michal Kruczkowski","email":"data:image/png;base64,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","orcid":"","institution":"Bydgoszcz University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Michal","middleName":"","lastName":"Kruczkowski","suffix":""}],"badges":[],"createdAt":"2025-04-06 18:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6388221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6388221/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101837176,"identity":"d81020aa-1113-4300-9d42-f1ac1ab27ced","added_by":"auto","created_at":"2026-02-04 07:43:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":776162,"visible":true,"origin":"","legend":"","description":"","filename":"SciRep042025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6388221/v1_covered_7d93cf82-5ab5-4b5e-a8c5-15db60f80a06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eData mining techniques for increasing resolution of refractive index measurements for precise cervical intraepithelial neoplasia and cervical cancer classification\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6388221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6388221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cervical cancer remains a critical global health issue, with early detection and precise classification of cervical intraepithelialneoplasia (CIN) essential for effective management. 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