Optimum machine learning models for osteosarcoma cancer detection and classification

Optimum machine learning models for osteosarcoma cancer detection and 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 Research Article Optimum machine learning models for osteosarcoma cancer detection and classification Amoakoh Gyasi-Agyei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4670466/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 Osteosarcoma is a bone-forming tumor which is more common with children and young adults than adults. Timely detection and classification of its type is crucial to its proper treatment and possible survival. Machine learning models, trained on datasets of the disease, are more effective detection and classification tool than hand-crafted features which are highly dependent on pathologists’ expertise. Publicly available raw osteosarcoma dataset was explored and preprocessed (including data denoising and data normalization). Three different datasets were then derived: the preprocessed dataset, and the preprocessed dataset with features selected via principal component analysis and a combination of analysis of variance and mutual information gain. Using the three datasets and eight machine learning (ML) algorithms, this study proposed three sets of optimum ML models (altogether 24 models) with their hyperparameters optimized using grid search. Then, the learned ML models were compared and validated using repeated stratified 10-fold cross-validation and 5 × 2 cross-validation paired t-test to select the best for our task. The ML model based on k-nearest neighbors algorithm proved to be the best, as it detected and classified osteosarcoma cancer in 344 ms with 100% Top-1 accuracy and F1- score and zero Type I and Type II errors. This performance exceeds those of existing algorithms for osteosarcoma cancer prediction. Thus, the proposed models are promising cutting-edge techniques for detecting osteosarcoma cancer to aid timely diagnosis, prognosis and treatment. Osteosarcoma classification AI in healthcare cancer detection healthcare informatics medical data mining 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-4670466","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325800364,"identity":"a90aecf8-4027-446c-abe8-939d3badfeb0","order_by":0,"name":"Amoakoh Gyasi-Agyei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYNACAxDB2PgAwkggXkuzAYOBAbFaIIBNAqKZgBb+/jWGH38U2NltON7cVnWj4A8DP3uOAcPPNtxaJG68MZbmMUhO3nDmYNvtHKDDJHveGDD24tHCcOOMgTSDAXOywY1EiBaDG0BbePFokb9xxvjnD4P6ZIP7D9uKQVrsgVoY/+LRYnC+x0yCx+CwncENxjZmsC0SOQbM+GwxvMFWZs1jcDxB8kxis3SOgTGPxJlnBYdlzuHWInf+8OabP/5U2/MdP/7wc84fOTn+9uSND9+U4fG+RAY4HhMXHIDweUDEATwagBFz/AGIspdvwKtsFIyCUTAKRjIAAG6dVElMMzoXAAAAAElFTkSuQmCC","orcid":"","institution":"Melbourne Institute of Technology (MIT)","correspondingAuthor":true,"prefix":"","firstName":"Amoakoh","middleName":"","lastName":"Gyasi-Agyei","suffix":""}],"badges":[],"createdAt":"2024-07-01 23:27:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4670466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4670466/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61904741,"identity":"3ce29a98-8a62-47be-952a-0f472a116627","added_by":"auto","created_at":"2024-08-07 01:17:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8077109,"visible":true,"origin":"","legend":"","description":"","filename":"mainbbl.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4670466/v1_covered_d9f08ffa-4133-4af5-b9c2-12d01d945eba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimum machine learning models for osteosarcoma cancer detection and classification","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":"Osteosarcoma classification, AI in healthcare, cancer detection, healthcare informatics, medical data mining","lastPublishedDoi":"10.21203/rs.3.rs-4670466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4670466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOsteosarcoma is a bone-forming tumor which is more common with children and young adults than adults. 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