Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques

Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques | 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 Utilizing Support Vector Machine Algorithm and Feature Reduction for Accurate Breast Cancer Detection An Exploration of Normalization and Hyperparameter Tuning Techniques VALABOJU SHIVA KUMAR CHARY This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3531811/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract In this work, we will evaluate the impact of independent component analysis (ICA) on a breast cancer decision support system's feature reduction capabilities. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset will be utilised to construct a one-dimensional feature vector (IC). We will study the performance of k-NN, ANN, RBFNN, and SVM classifiers in spotting mistakes using the original 30 features. Additionally, we will compare the IC-recommended classification with the original feature set using multiple validation and division approaches. The classifiers will be tested based on specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and receiver operating characteristic (ROC) curve. This effort attempts to boost the medical decision support system's efficiency while minimising computational complexity. Artificial Intelligence and Machine Learning Independent Component Analysis (ICA) Breast Cancer Decision Support System Feature Reduction Wisconsin Diagnostic Breast Cancer (WDBC) dataset One-Dimensional Feature Vector k-NN ANN RBFNN SVM Classification Original 30 Features IC-Recommended Classification Validation Division Approaches Specificity Sensitivity Accuracy F-score Youden's Index Discriminant Power Receiver Operating Characteristic (ROC) Curve Medical Decision Support System Computational Complexity Efficiency Improvement Full Text Additional Declarations Competing interests: The authors declare no competing interests. I had full access to all of the data in this study and I take complete responsibility for the integrity of the data and the accuracy of the data analysis. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-3531811","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":245441077,"identity":"91aa479c-6e3d-4345-9ad3-d630c84701bb","order_by":0,"name":"VALABOJU SHIVA KUMAR CHARY","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABa0lEQVRIie2RMUvDQBSArwQuy2nWC6n+hpRCFfJTXHoI7XIBxw6lphzEJdg1UjB/IS6dHFIOo0NwDujQUsggFQoFUSziXRGNpu6C+YbH3bv77r3HAVBS8idRHAUACACuOABE6xSU4XigMrnF1YJS+UWpnHlxUyposyKPZMgrYUpNufipmNfj/vKos02CIXPxc8IPtCHPJi+XXNF9unxIu/sIqPwqzCkJYYafQBLej13dS7nt37b2al7GoYYeRxaNRWOo1Uq/FN0hjrHlCgUTZqIFt50EQYwijvQTe1SnYi3GaeSVwZS9SiXwCauthBIIRV9FHJsRzer0raBomLjrKk5K+jMkGguFYogqpnlDlZntblCmriVmqYdSqSZt+yKBDaMatZu6FzcU+xQj+H0WqB3yu6NOvBP47cl4Hlv2eaJk+jyymprKZkv61NvVVB7nlA/iQoatH8Sf31SgW8j0ZFAWG2+XlJSU/DPeAcGVjaGcd00PAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-8822-6942","institution":"KL University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"VALABOJU","middleName":"SHIVA KUMAR","lastName":"CHARY","suffix":""}],"badges":[],"createdAt":"2023-11-01 06:44:45","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-3531811/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3531811/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46036174,"identity":"0806b52b-252f-4964-8d89-c180e7316cf8","added_by":"auto","created_at":"2023-11-07 19:00:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1465537,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerUtilizingSupportVectorMachineAlgorithmandFeatureReductionforAccurateBreastCancerDetectionAnExplorationofNormalizationandHyperparameterTuningTechniques.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3531811/v2_covered_04374781-2ce1-4333-830d-ff9173f8357f.pdf"}],"financialInterests":"\u003cp\u003eCompeting interests: The authors declare no competing interests. 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