{"paper_id":"4862a09a-e271-414f-a2c0-57ef3fe649f5","body_text":"Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things | 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 Evolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things Clement Nyirenda, Richard Chilipa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4729499/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 Evolving neuro-fuzzy systems (ENFS) have shown great promise in analysis of streaming data, integrating artificial neural networks and fuzzy logic to create self-learning, data-driven models suitable for online (real-time) applications. In healthcare, ENFS have the potential to transform the Internet of Medical Things (IoMT) by enabling real-time data processing, decision support, and timely emergency response. In this study six evolving fuzzy min-max neural network models have been implemented using Python and Kafka, and evaluated on the MIT-BIH Arrhythmia dataset comprising 109,446 samples corresponding to shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction. To achieve online continual learning, the prequential test-then-train method has been used to sequentially classify the data instances from scratch and update the system structure along with the arrival of new data samples. The Enhanced Fuzzy Min-Max Neural Network (EFMMNN) achieved the highest accuracy of 98.11%. Future work will explore deploying these evolving fuzzy systems in real-world healthcare settings using edge-fog computing. Artificial Intelligence and Machine Learning Biomedical Engineering evolving neuro-fuzzy systems stream processing online continual learning Apache Kafka multiclass classification Full Text Additional Declarations The authors declare no competing interests. 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-4729499\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":326087795,\"identity\":\"e559adaa-7ba8-4957-bfd6-52421d7b4bdf\",\"order_by\":0,\"name\":\"Clement Nyirenda\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJCCA0DM2MDeQLIWngMk2sTYIJFApFL+2T2Ghysq7sj2z3xjJvGDwU6egf/wA7xaJO6cMTh45swz4xm3c8wkexiSDRsYjhng1WIgkWNwsLHtcGIDUMttBgbmBKAbidHy73Di/JtnQFrqExiY2T8QoaXhcOKGGzwgLYcTGNh48NsicSOt4GDDscPGG8+klf/sMThu2MbDU4BXC/+M5M0fG2oOy847fnizwY+Kanl+/uMb8GphYOBAdgaQzUZAPRCwPyCsZhSMglEwCkY2AAAgMkjYLdcmpAAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0000-0002-4181-0478\",\"institution\":\"University of the Western Cape\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Clement\",\"middleName\":\"\",\"lastName\":\"Nyirenda\",\"suffix\":\"\"},{\"id\":326087796,\"identity\":\"10194a31-3465-4734-ad24-69e392369780\",\"order_by\":1,\"name\":\"Richard Chilipa\",\"email\":\"\",\"orcid\":\"https://orcid.org/0009-0002-4365-0743\",\"institution\":\"University of the Western Cape\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Richard\",\"middleName\":\"\",\"lastName\":\"Chilipa\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-07-12 10:08:03\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-4729499/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4729499/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":60301496,\"identity\":\"db261ad9-994f-4e6a-8e55-fb976e53cf52\",\"added_by\":\"auto\",\"created_at\":\"2024-07-15 10:58:30\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":384470,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ChilipaConferencePaper.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4729499/v1_covered_b8538760-fd92-48e7-a713-1018a70fc4ec.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eEvolving Neuro-Fuzzy Systems for Real-Time Data Processing in Internet of Medical Things\\u003c/p\\u003e\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"University of the Western Cape\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"evolving neuro-fuzzy systems, stream processing, online continual learning, Apache Kafka, multiclass classification\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4729499/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4729499/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eEvolving neuro-fuzzy systems (ENFS) have shown great promise in analysis of streaming data, integrating artificial\\u003c/p\\u003e\\n\\u003cp\\u003eneural networks and fuzzy logic to create self-learning, data-driven models suitable for online (real-time) applications. 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