Kullback-Leibler Divergence in Feature Selection: A Methodology for Improved Detection of Heart Valve Disorders

Kullback-Leibler Divergence in Feature Selection: A Methodology for Improved Detection of Heart Valve Disorders | 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 Kullback-Leibler Divergence in Feature Selection: A Methodology for Improved Detection of Heart Valve Disorders PREETHA V H, RAGHU C V This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5966463/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Dimensionality reduction is crucial for effectively managing high-dimensional datasets, particularly in the healthcare industry. Feature selection identifies the most relevant attributes, reducing computational overhead and ensuring robust performance, especially in resource-constrained environments. This study introduces a Kullback-Leibler Divergence (KLD)-based feature selection method for heart sound analysis aimed at diagnosing valvular heart diseases. Mel Frequency Cepstral Coefficients (MFCC) and Mel spectrograms were extracted from the dataset as input features. KLD was applied to identify the most informative features, which were subsequently validated using various classifiers. This approach resulted in an accuracy of 99% in diagnosing five distinct heart sounds, outperforming the classifiers using the full feature set. The method prioritized critical features, leading to improved performance across all evaluated classifiers. This endeavor also aims to classify heart sounds on an embedded platform, enabling the efficient analysis and accurate diagnosis of cardiovascular conditions. These findings highlight the potential of KLD-based feature selection for the real-time detection of heart valve disorders. By reducing computational complexity while preserving classification accuracy this approach supports the development of efficient, cost-effective edge-based tools, ultimately improving diagnostic precision and healthcare resource efficiency. Feature selection Kullback-Leibler divergence Valvular heart diseases Mel frequency cepstral coefficients Mel spectrograms Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 Jul, 2025 Reviewers invited by journal 01 May, 2025 Editor invited by journal 22 Apr, 2025 Editor assigned by journal 26 Mar, 2025 First submitted to journal 24 Mar, 2025 Editorial decision: Major revisions 13 Feb, 2025 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-5966463","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450805242,"identity":"f2c543ec-94ca-48a7-9302-7983f61bb326","order_by":0,"name":"PREETHA V H","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0008-7787-4554","institution":"NITC: National Institute of Technology Calicut","correspondingAuthor":true,"prefix":"","firstName":"PREETHA","middleName":"V","lastName":"H","suffix":""},{"id":450805243,"identity":"eb5530dc-f165-41d2-b059-7778e7b3a011","order_by":1,"name":"RAGHU C V","email":"","orcid":"","institution":"NITC: National Institute of Technology Calicut","correspondingAuthor":false,"prefix":"","firstName":"RAGHU","middleName":"C","lastName":"V","suffix":""}],"badges":[],"createdAt":"2025-02-05 14:21:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5966463/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5966463/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82325924,"identity":"48346e90-2dd2-4f77-9c5e-dece4bfb1a77","added_by":"auto","created_at":"2025-05-09 06:12:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":833860,"visible":true,"origin":"","legend":"","description":"","filename":"APESD2500066R2reviewer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5966463/v1_covered_001a664d-0708-4ffa-85a4-b882cf7c8bc6.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eKullback-Leibler Divergence in Feature Selection: A Methodology for Improved Detection of Heart Valve Disorders\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Feature selection, Kullback-Leibler divergence, Valvular heart diseases, Mel frequency cepstral coefficients, Mel spectrograms","lastPublishedDoi":"10.21203/rs.3.rs-5966463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5966463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDimensionality reduction is crucial for effectively managing high-dimensional datasets, particularly in the healthcare industry. Feature selection identifies the most relevant attributes, reducing computational overhead and ensuring robust performance, especially in resource-constrained environments. This study introduces a Kullback-Leibler Divergence (KLD)-based feature selection method for heart sound analysis aimed at diagnosing valvular heart diseases. Mel Frequency Cepstral Coefficients (MFCC) and Mel spectrograms were extracted from the dataset as input features. KLD was applied to identify the most informative features, which were subsequently validated using various classifiers. This approach resulted in an accuracy of 99% in diagnosing five distinct heart sounds, outperforming the classifiers using the full feature set. The method prioritized critical features, leading to improved performance across all evaluated classifiers. This endeavor also aims to classify heart sounds on an embedded platform, enabling the efficient analysis and accurate diagnosis of cardiovascular conditions. These findings highlight the potential of KLD-based feature selection for the real-time detection of heart valve disorders. By reducing computational complexity while preserving classification accuracy this approach supports the development of efficient, cost-effective edge-based tools, ultimately improving diagnostic precision and healthcare resource efficiency.\u003c/p\u003e","manuscriptTitle":"Kullback-Leibler Divergence in Feature Selection: A Methodology for Improved Detection of Heart Valve Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 05:39:16","doi":"10.21203/rs.3.rs-5966463/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-14T05:41:33+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T23:19:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Physical and Engineering Sciences in Medicine","date":"2025-04-23T02:17:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-26T06:33:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Physical and Engineering Sciences in Medicine","date":"2025-03-25T03:24:16+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revisions","date":"2025-02-14T01:14:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"physical-and-engineering-sciences-in-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apes","sideBox":"Learn more about [Physical and Engineering Sciences in Medicine](http://link.springer.com/journal/13246)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/apes/default.aspx","title":"Physical and Engineering Sciences in Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0dee3299-9464-4257-b9ab-34b8340595ab","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:17:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 05:39:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5966463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5966463","identity":"rs-5966463","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}