An approach for Parkinson's disease detection based on Artificial Inteligence techniques

An approach for Parkinson's disease detection based on Artificial Inteligence 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 Article An approach for Parkinson's disease detection based on Artificial Inteligence techniques Marcos Batista Figueredo, Roberto Luiz Souza Monteiro, Alexandre do Nascimento Silva, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4468316/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 Parkinson’s disease (PD) is a neurodegenerative disorder caused by a lack of dopamine secretion, resulting in motor dysfunction that affects speech production. The literature indicates that PD can be identified by observing changes in speech signals over time. Early detection of PD is essential to slow its progression and enable patients to access disease-modifying therapies. In this context, an adapted Convolutional Neural Network (CNN) for this task appears promising. In this paper, we propose a CNN classifier to efficiently detect PD with an accuracy above 95%. We employed advanced machine learning techniques to identify PD early through voice analysis using the Parkinson’s Disease Voice Recording Data Set, which includes the vocal characteristics of 147 PD patients and 48 healthy individuals. The primary objective is to enhance the diagnostic accuracy of PD using a CNN tailored for this task. The preprocessing steps involved cleaning and normalizing the data, followed by the application of Bayesian hyperparameter optimization to improve model performance. The CNN model achieved an average accuracy of 96.8451% in PD detection, demonstrating high precision, sensitivity, and a balanced F1 score. This result underscores the model’s effectiveness in identifying individuals with PD and highlights the potential of using a single CNN topology, as opposed to more complex hybrid models, to achieve satisfactory diagnostic rates.To evaluate the performance of the proposed approach, we used simulated data generated from the original dataset. The results indicate that the proposed approach is suitable for the automatic identification of PD in practical scenarios. Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Computational models 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-4468316","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312221693,"identity":"cd6322fa-21f9-4cd0-be38-a12f9b268c17","order_by":0,"name":"Marcos Batista Figueredo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCcYHByCsBMYHIJJBgqAWZgOYFmYDorVAWQlsEkRp0Z3dzHjwa5tNPj978rFq3h02eQzSvQ/wajG7c5jhsGxbmuXMnmdpt3nPpBUzyBw3wK/lRv6Bw5Jthw0MbuSY3eZtO5zYIJGG32FmN5IZgFr+G9gDtRTztv0nTsvBj20HDAwkcsyYedsOEKEF5BeGc8kGEmeeJUvObUsuZpM5RkDL7Wbmjz/K7Az425MPfnjbZpfHL92GXwsIMPMg89gIa2BgYPxBjKpRMApGwSgYuQAAfzVIxxOAKqkAAAAASUVORK5CYII=","orcid":"","institution":"Bahia State University","correspondingAuthor":true,"prefix":"","firstName":"Marcos","middleName":"Batista","lastName":"Figueredo","suffix":""},{"id":312221694,"identity":"ec1a2bfa-f0de-4b40-8642-bbe31d44c33b","order_by":1,"name":"Roberto Luiz Souza Monteiro","email":"","orcid":"","institution":"SENAI CIMATEC University Center","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"Luiz Souza","lastName":"Monteiro","suffix":""},{"id":312221696,"identity":"dc2cdbde-7e76-48db-9a72-0aa0fd2a3df7","order_by":2,"name":"Alexandre do Nascimento Silva","email":"","orcid":"","institution":"State University of Santa Cruz","correspondingAuthor":false,"prefix":"","firstName":"Alexandre","middleName":"do Nascimento","lastName":"Silva","suffix":""},{"id":312221699,"identity":"be464f1d-6179-49b6-af47-8d6cce48030c","order_by":3,"name":"Leandro Brito Santos","email":"","orcid":"","institution":"Federal University of Recôncavo da Bahia","correspondingAuthor":false,"prefix":"","firstName":"Leandro","middleName":"Brito","lastName":"Santos","suffix":""},{"id":312221701,"identity":"bc6d6459-2497-4a6e-8519-6311b480ec85","order_by":4,"name":"José Roberto de Araújo Fontoura","email":"","orcid":"","institution":"Bahia State University","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Roberto de Araújo","lastName":"Fontoura","suffix":""},{"id":312221702,"identity":"721848a4-546d-46ac-be7a-2b6f09dac192","order_by":5,"name":"Thiago Barros Murari","email":"","orcid":"","institution":"SENAI CIMATEC University Center","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"Barros","lastName":"Murari","suffix":""}],"badges":[],"createdAt":"2024-05-23 17:01:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4468316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4468316/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67175696,"identity":"59304a07-713c-48c7-91e1-18aa9d3c4a0d","added_by":"auto","created_at":"2024-10-22 04:54:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":546313,"visible":true,"origin":"","legend":"","description":"","filename":"mhcywnrmbgrchbdgxnqcxmwhwqszyxdq.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4468316/v1_covered_9889fe83-2f7a-4076-81da-532fe5a15205.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An approach for Parkinson's disease detection based on Artificial Inteligence techniques","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4468316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4468316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Parkinson’s disease (PD) is a neurodegenerative disorder caused by a lack of dopamine secretion, resulting in motor dysfunction that affects speech production. The literature indicates that PD can be identified by observing changes in speech signals over time. Early detection of PD is essential to slow its progression and enable patients to access disease-modifying therapies. In this context, an adapted Convolutional Neural Network (CNN) for this task appears promising. In this paper, we propose a CNN classifier to efficiently detect PD with an accuracy above 95\\%. We employed advanced machine learning techniques to identify PD early through voice analysis using the Parkinson’s Disease Voice Recording Data Set, which includes the vocal characteristics of 147 PD patients and 48 healthy individuals. The primary objective is to enhance the diagnostic accuracy of PD using a CNN tailored for this task. The preprocessing steps involved cleaning and normalizing the data, followed by the application of Bayesian hyperparameter optimization to improve model performance. The CNN model achieved an average accuracy of 96.8451\\% in PD detection, demonstrating high precision, sensitivity, and a balanced F1 score. This result underscores the model’s effectiveness in identifying individuals with PD and highlights the potential of using a single CNN topology, as opposed to more complex hybrid models, to achieve satisfactory diagnostic rates.To evaluate the performance of the proposed approach, we used simulated data generated from the original dataset. 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