Enhancing Parkinson’s Diagnosis with PhonatoryVoice Analysis: Optimizing Machine LearningFeature Selection for Support Vector Machines

Enhancing Parkinson’s Diagnosis with PhonatoryVoice Analysis: Optimizing Machine LearningFeature Selection for Support Vector Machines | 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 Article Enhancing Parkinson’s Diagnosis with PhonatoryVoice Analysis: Optimizing Machine LearningFeature Selection for Support Vector Machines Wiput Thirapanish, Pittipol Kantavat, Dittaya Wanvarie, Ekapol Chuangsuwanich, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8184731/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Parkinson’s disease (PD) is a common neurological disorder that impairs mobility and significantly impacts patients’ quality of life. Early diagnosis of PD is crucial for timely intervention and management. This study explores the potential of computer-aided analysis, specifically machine learning, for early PD diagnosis, focusing on phonatory voice analysis using dysphonia measures from sustained vowels. To enhance PD diagnosis performances, this study investigates the effectiveness of two feature selection methods, L1-norm support vector machine (L1-norm SVM) and Recursive Feature Elimination (RFE), within the framework of Support Vector Machine (SVM) and data from UCI Machine Learning Repository and Chulalongkorn Centre of Excellence for Parkinson’s Disease \& Related Disorders, a leading medical center for treating PD patients in Thailand, were utilized. Furthermore, this research uses PD voice samples to analyze dysphonia features for diagnosing PD. The results suggest that both L1-norm SVM and RFE enhance the accuracy of PD diagnosis, with L1-norm SVM showing higher performance, achieving an accuracy of 95.97%, a positive recall score of 97.16%, and an F1-macro score of 95.69%. The study also highlights the importance of key dysphonia measures, including RPDE, DFA, and HNR, in differentiating PD voices from non-PD voices. Further research is needed to explore additional feature selection methods, audio features, and datasets, which would help uncover more characteristics in early PD voice analysis and further enhance the performance of PD diagnosis. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Health sciences/Neurology Biological sciences/Neuroscience Parkinson's disease (PD) machine learning classification feature selection dysphonia Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviews received at journal 13 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviews received at journal 05 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor invited by journal 28 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 23 Nov, 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-8184731","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555174838,"identity":"a20b0729-079f-46a8-b090-2ec3f4d64583","order_by":0,"name":"Wiput Thirapanish","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Wiput","middleName":"","lastName":"Thirapanish","suffix":""},{"id":555174839,"identity":"77f466dd-d929-464d-89ab-6e72cc53530a","order_by":1,"name":"Pittipol Kantavat","email":"","orcid":"","institution":"Chulalongkorn 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Machines","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Parkinson's disease (PD), machine learning, classification, feature selection, dysphonia","lastPublishedDoi":"10.21203/rs.3.rs-8184731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8184731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a common neurological disorder that impairs mobility and significantly impacts patients\u0026rsquo; quality of life. Early diagnosis of PD is crucial for timely intervention and management. This study explores the potential of computer-aided analysis, specifically machine learning, for early PD diagnosis, focusing on phonatory voice analysis using dysphonia measures from sustained vowels. To enhance PD diagnosis performances, this study investigates the effectiveness of two feature selection methods, L1-norm support vector machine (L1-norm SVM) and Recursive Feature Elimination (RFE), within the framework of Support Vector Machine (SVM) and data from UCI Machine Learning Repository and Chulalongkorn Centre of Excellence for Parkinson\u0026rsquo;s Disease \\\u0026amp; Related Disorders, a leading medical center for treating PD patients in Thailand, were utilized. Furthermore, this research uses PD voice samples to analyze dysphonia features for diagnosing PD. The results suggest that both L1-norm SVM and RFE enhance the accuracy of PD diagnosis, with L1-norm SVM showing higher performance, achieving an accuracy of 95.97%, a positive recall score of 97.16%, and an F1-macro score of 95.69%. The study also highlights the importance of key dysphonia measures, including RPDE, DFA, and HNR, in differentiating PD voices from non-PD voices. Further research is needed to explore additional feature selection methods, audio features, and datasets, which would help uncover more characteristics in early PD voice analysis and further enhance the performance of PD diagnosis.\u003c/p\u003e","manuscriptTitle":"Enhancing Parkinson’s Diagnosis with PhonatoryVoice Analysis: Optimizing Machine LearningFeature Selection for Support Vector Machines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 05:29:06","doi":"10.21203/rs.3.rs-8184731/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-22T05:41:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T09:21:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-15T20:02:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-13T19:12:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250991027741044714170587773914108190216","date":"2025-12-06T05:46:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T12:39:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337023896438404940720394172131153974760","date":"2025-12-05T12:22:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243461197314029414547880601947092554247","date":"2025-12-04T10:12:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232939007161472010176415304503345812507","date":"2025-12-03T14:41:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-03T12:29:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-28T15:24:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T06:27:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T06:24:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-23T10:03:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1030c2e9-95e5-4147-b984-9a1b45fea381","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":59083089,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59083090,"name":"Health sciences/Diseases"},{"id":59083091,"name":"Health sciences/Health care"},{"id":59083092,"name":"Health sciences/Neurology"},{"id":59083093,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-12-22T05:54:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 05:29:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8184731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8184731","identity":"rs-8184731","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}