Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique

Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique | 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 Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique Zulfikar Ali Ansari, Manish Madhava Tripathi, Rafeeq Ahmad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3860791/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 Breast cancer continues to be a prominent issue in global health, requiring the implementation of novel approaches for the timely identification and assessment of the disease. Machine learning has been extensively integrated into the field of breast cancer diagnostics to gain profound insights and enhance the precision and efficacy of recognizing potential instances of breast cancer. Given the global nature of this disease, the early detection of cancer continues to pose a considerable problem. Our study introduces an ensemble strategy that integrates the results of Dimensionality Reduction (DR) approaches, namely Principal Component Analysis (PCA), Non-negative matrix factorization (NMF), and Value Decomposition (SVD), and subsequently inputs them into a resilient classification algorithm. In this study, we examine many algorithms, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP), to evaluate their diagnostic accuracy. Our findings show that MLP, LR, and SVM have a maximum accuracy of 97.9%, but MLP performance varies when used with NMF & PCA, which is 97.20%. LR also produced good accuracy with NMF and PCA, which is 97.9%, but again, performance is reduced when used with SVD. The SVM gives a consistent result with PCA, SVD, and NMF, which is 97.9%. Breast Cancer Dimensionality Reduction Ensemble Learning Machine Learning 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-3860791","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267607659,"identity":"2b8c74ba-7ae3-45d1-b39a-e8be778b3cfc","order_by":0,"name":"Zulfikar Ali Ansari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFAC5oYPif9s5PjZmw8AeRIyRGhhbJyRwJZmLNlzLAGkhYc4LQxshxMNbuQYgLiEtcjPSGxseMCTlmBwIOfzqxs1FjwM7IePbsBrB0hLgoRNnuSBs9usc44BHcaTlnYDnxZmicT2BwkGacV8B3u3GeewAbVI8Jjh1cImAbIl4XBiw2GeZ8Y5/4jQwgPWcuBw4oRjPMyPc9uI0CLB87CxIbEBFMhsZsy5fRI8bIT8It+efLDxZwMwKuUfP/6c860OGKeHj+HVguovMEmschBg/kCK6lEwCkbBKBg5AACgMUxVRX/XVAAAAABJRU5ErkJggg==","orcid":"","institution":"Integral University","correspondingAuthor":true,"prefix":"","firstName":"Zulfikar","middleName":"Ali","lastName":"Ansari","suffix":""},{"id":267607660,"identity":"d42efd60-2d0e-4825-b066-abcdfe95f6e0","order_by":1,"name":"Manish Madhava Tripathi","email":"","orcid":"","institution":"Integral University","correspondingAuthor":false,"prefix":"","firstName":"Manish","middleName":"Madhava","lastName":"Tripathi","suffix":""},{"id":267607661,"identity":"876e51a8-761c-4eed-a4ae-c38875588efa","order_by":2,"name":"Rafeeq Ahmad","email":"","orcid":"","institution":"Koneru Lakshmaiah Education Foundation","correspondingAuthor":false,"prefix":"","firstName":"Rafeeq","middleName":"","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2024-01-13 17:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3860791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3860791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51201437,"identity":"829c7b8c-4e44-4c31-9a37-abcd68d1596e","added_by":"auto","created_at":"2024-02-15 22:23:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":535959,"visible":true,"origin":"","legend":"","description":"","filename":"EvolutionaryIntelligenceManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3860791/v1_covered_fcaa08fb-15bc-4493-8023-6242663436b9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique","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":"Breast Cancer, Dimensionality Reduction, Ensemble Learning, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-3860791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3860791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast cancer continues to be a prominent issue in global health, requiring the implementation of novel approaches for the timely identification and assessment of the disease. Machine learning has been extensively integrated into the field of breast cancer diagnostics to gain profound insights and enhance the precision and efficacy of recognizing potential instances of breast cancer. Given the global nature of this disease, the early detection of cancer continues to pose a considerable problem. Our study introduces an ensemble strategy that integrates the results of Dimensionality Reduction (DR) approaches, namely Principal Component Analysis (PCA), Non-negative matrix factorization (NMF), and Value Decomposition (SVD), and subsequently inputs them into a resilient classification algorithm. In this study, we examine many algorithms, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP), to evaluate their diagnostic accuracy. Our findings show that MLP, LR, and SVM have a maximum accuracy of 97.9%, but MLP performance varies when used with NMF \u0026amp; PCA, which is 97.20%. LR also produced good accuracy with NMF and PCA, which is 97.9%, but again, performance is reduced when used with SVD. The SVM gives a consistent result with PCA, SVD, and NMF, which is 97.9%.\u003c/p\u003e","manuscriptTitle":"Integrated Ensemble Strategy for Breast Cancer Detection using Dimensionally Reduction Technique","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 05:37:08","doi":"10.21203/rs.3.rs-3860791/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":"f7c29df3-56ba-41cb-809b-91a1d6c5e368","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-15T22:15:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 05:37:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3860791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3860791","identity":"rs-3860791","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}