Brain Tumor Classification in Sustainable Healthcare using Machine Learning

Brain Tumor Classification in Sustainable Healthcare using Machine Learning | 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 Brain Tumor Classification in Sustainable Healthcare using Machine Learning Sanjay Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5795846/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 In sustainable healthcare, early and accurate diagnosis is crucial in reducing the burden on healthcare systems and improving patient outcomes. This paper explores the application of machine learning techniques, specifically K-Nearest Neighbors (KNN) and Support Vector Classifier (SVC), for classifying brain tumors based on medical imaging data. By leveraging these machine learning methods, we aim to provide an efficient and interpretable solution that maintains high accuracy while optimizing computational resources. Machine learning models, due to their lower resource demands, are more suitable than deep learning approaches for sustainable healthcare environments, particularly in scenarios with limited infrastructure. The proposed models are validated through cross validation and hyperparameter tuning to achieve optimal performance. Our results demonstrate the potential of machine learning in brain tumor classification, offering a balance between accuracy and sustainability, and paving the way for more scalable and accessible diagnostic systems. brain deep learning 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-5795846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":399909928,"identity":"889a3cc4-c974-4bea-8e48-ca2c789adba8","order_by":0,"name":"Sanjay Patel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYJACAwjF3P5BogJEMzcQp4WHgbGNweIMSAsjYS0McC2VbSAmAS3y7WcPFPxsuyNvz97Y9uDmvNpo/naglh8V23A76kxegmFv2zPDHp6D7YYztx3PnXGYsYGx58xtPP7IMTDgbTvM2COR2CAtue1YbgNQCzNjG24t8v1vDAz/th2275F/2CD9d86x3PmEtDDcyDEwBtqS2CPB2CYh2VCTu4GQFoMbbwyMZc49S+45k9hsIHHsQO5GoJaD+Pwi359jZvim7I5te/vhgw8kaupy550HMn5U4HEYAwObASPbARjnMJg8gF0lHDA/YPgDV1NHQPEoGAWjYBSMRAAAzqBiozh7tU4AAAAASUVORK5CYII=","orcid":"","institution":"Nirma University","correspondingAuthor":true,"prefix":"","firstName":"Sanjay","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2025-01-09 11:00:29","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-5795846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5795846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73452593,"identity":"81f945a2-fa8a-46ad-9968-a4fdeb2b79f2","added_by":"auto","created_at":"2025-01-10 06:23:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":339683,"visible":true,"origin":"","legend":"","description":"","filename":"Brain.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5795846/v1_covered_15f13a25-c834-4c56-9869-5bc747a757ad.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBrain Tumor Classification in Sustainable Healthcare using Machine Learning\u003c/p\u003e","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":"brain, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-5795846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5795846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn sustainable healthcare, early and accurate diagnosis is crucial in reducing the burden on healthcare systems and improving patient outcomes. This paper explores the application of machine learning techniques, specifically K-Nearest Neighbors (KNN) and Support Vector Classifier (SVC), for classifying brain tumors based on medical imaging data. By leveraging these machine learning methods, we aim to provide an efficient and interpretable solution that maintains high accuracy while optimizing computational resources. Machine learning models, due to their lower resource demands, are more suitable than deep learning approaches for sustainable healthcare environments, particularly in scenarios with limited infrastructure. The proposed models are validated through cross validation and hyperparameter tuning to achieve optimal performance. Our results demonstrate the potential of machine learning in brain tumor classification, offering a balance between accuracy and sustainability, and paving the way for more scalable and accessible diagnostic systems.\u003c/p\u003e","manuscriptTitle":"Brain Tumor Classification in Sustainable Healthcare using Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 05:59:30","doi":"10.21203/rs.3.rs-5795846/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":"9c6eb87c-e0f7-4c63-9fbf-5a61e046eab9","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-10T05:59:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 05:59:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5795846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5795846","identity":"rs-5795846","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}