Comparative Analysis of Machine Learning Models to Predict Backfat Thickness in Hanwoo Cattle

Comparative Analysis of Machine Learning Models to Predict Backfat Thickness in Hanwoo Cattle | 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 Comparative Analysis of Machine Learning Models to Predict Backfat Thickness in Hanwoo Cattle Taeyong Yun, Dawoon Jeong, Jinhyeon Yun, Woongsup Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9026112/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Accurate prediction of carcass traits is essential for genetic improvement and value optimization in the beef industry. Backfatthickness is particularly important because it directly affects both market value and consumer preference. This study evaluatedthe predictive performance of machine learning (ML) models for estimating backfat thickness in Hanwoo cattle. A total of 386 Hanwoo carcass records were used, and 10 carcass features served as input variables, with backfat thickness being the prediction target. Model performance was assessed primarily by mean absolute error (MAE) and secondarily by the coefficient of determination (R2). Support vector regression (SVR) showed the best predictive performance, achieving the lowest MAE(2.796) and the highest R2 (0.375) after hyperparameter tuning. In contrast, other models showed higher MAE values, generally ranging from 3.0 to 4.0. Pearson correlation analysis identified intramuscular fat score (r = 0.37), quality grade (r = −0.33), andsex (r = 0.34) as the most influential predictors of backfat thickness. These findings support SVR as a robust approach for predicting carcass backfat thickness in Hanwoo cattle and provide a practical framework for ML-based precision phenotyping and breeding strategies. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviewers agreed at journal 15 May, 2026 Reviews received at journal 13 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 12 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Editor invited by journal 10 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 06 Mar, 2026 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. 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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-9026112","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":606499717,"identity":"290f424d-cc75-4ddc-8f23-bb32b2cae691","order_by":0,"name":"Taeyong Yun","email":"","orcid":"","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Taeyong","middleName":"","lastName":"Yun","suffix":""},{"id":606499720,"identity":"b4221fda-2640-4ebc-a5ff-dc346354fdd7","order_by":1,"name":"Dawoon Jeong","email":"","orcid":"","institution":"Jeonbuk National University","correspondingAuthor":false,"prefix":"","firstName":"Dawoon","middleName":"","lastName":"Jeong","suffix":""},{"id":606499721,"identity":"329a0bd8-437c-44c5-b72e-b85275aa8d33","order_by":2,"name":"Jinhyeon Yun","email":"","orcid":"","institution":"Chonnam National University","correspondingAuthor":false,"prefix":"","firstName":"Jinhyeon","middleName":"","lastName":"Yun","suffix":""},{"id":606499723,"identity":"33642b36-991a-4deb-95f8-ad224aadc98f","order_by":3,"name":"Woongsup Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2RMWrDMBSGnxAoS0pWC6f0Ci4Gl1KXXqWi4CyNF0OnDA8M8pIDZPMVcoQEg7KYzllN1w4eCjXUhcr2kqFqyBaoPpD4Jd7HLxCAxXLWjPSq+0Sw273jCtWzq5MVOj44GxVvl6rqoy1ioHT7di/D+CpLEZoF+DdoUEo18y9lkQBlT/5cRolXbpEsFQTTze9KsH8OXI6FQDoO3LksxNoRCBcIoWN4WK84badMPr9utZLnFZLvIwqvWd/CKNEK7glS3RKYlIdSRS6Rs4RR5vPlayTWpcBiqhzfpPAsVbxp7+LJKK3q5iUUebarqvdFeL0yKB3ddzyyLhE23GwATB0DpNHKENs/By0Wi+Wf8gPfjVgIo8ve0gAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University","correspondingAuthor":true,"prefix":"","firstName":"Woongsup","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2026-03-04 05:39:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9026112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9026112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105033888,"identity":"b244c2d3-3052-4062-a9ae-c0e645abe971","added_by":"auto","created_at":"2026-03-20 07:22:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":415561,"visible":true,"origin":"","legend":"","description":"","filename":"ComparativeAnalysisofMachineLearningModelstoPredictBackfatThicknessinHanwooCattle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9026112/v1_covered_bb4f9fb5-655d-4b8d-b86f-57c5ddd6ebba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of Machine Learning Models to Predict Backfat Thickness in Hanwoo Cattle","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9026112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9026112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate prediction of carcass traits is essential for genetic improvement and value optimization in the beef industry. 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