Disease prediction model: An efficient machine learning- based DNA classifier

Disease prediction model: An efficient machine learning- based DNA classifier | 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 Disease prediction model: An efficient machine learning- based DNA classifier GARIMA MATHUR This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4248864/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 When it comes to health care, everyone is always eager to identify diseases in their early stages, but doing so might be difficult because of the lack of knowledge on the patterns of specific diseases since DNA contains most of the genetic blueprints, DNA sequence classification can be used to predict the existence of certain conditions accurately. There are several machine-learning techniques available to classify DNA sequences. Traits from known diseases are extracted to train the model for new, unknown diseases. The expansion of patients' access to digital platforms for early disease diagnosis through knowledge transfer to artificial neural networks eliminates the need for clinical equipment. To analyze the model, DNA samples of four well-known viruses—human respiratory viruses, lung cancer viruses, and papilla-maviruses (HPV)—are gathered from Genbank (NCBI). These samples are then compared with five existing methods using seven different parameters—specificity, accuracy, Matthews correlation coefficient, recall, precision, F1-score, area under the receiver operating characteristic (ROC) curve (AUROC), and area under the Precision-Recall (PRC) curve (AUPRC)—to facilitate the analysis of the model. The outcome demonstrates that the proposed work provides significantly better precision and accuracy than the prior best results, where precision has increased by more than 5.124% and accuracy has increased by about 15.9%. DNA sequences Classifiers Feature extraction Healthcare Machine Learning 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. 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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-4248864","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337629095,"identity":"564cecbd-73f5-46e2-8f76-38d390cf9e06","order_by":0,"name":"GARIMA MATHUR","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJCCA2BSAog/gEkI4CGohQeomHEGUAtMKV4tDDAtzDzEKJV3P/vw0I2abfL20j1mn23+WNjbszc/YPhRwyBjjkOL4Zl0g8M5x24b9sicMZ6d2yaR2MNzzICx5xgDj2UDDi0NaQyHc9huM/ZI5Bgz5zZIJPBI5DAw8DYw8BgcwKGl/xlQy7/b9mAtFn8k7Hnk3zAw/sWjRV4CaEtu2+1EsBYGNgmgdTwMzPhsMZAA2pLbdzu550ZaMWMvyC9n0gwOyxyTwG1Lfxrz55xvt23bZyRvZvjxp86evf3ww4dvamzscdqCVfwAA1IywLClAafUKBgFo2AUjAIoAABSXVSsLheYpwAAAABJRU5ErkJggg==","orcid":"","institution":"Rajiv Gandhi Technical University","correspondingAuthor":true,"prefix":"","firstName":"GARIMA","middleName":"","lastName":"MATHUR","suffix":""}],"badges":[],"createdAt":"2024-04-10 18:16:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4248864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4248864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62073883,"identity":"b03a5eb5-af51-4fde-ac81-59b79f659209","added_by":"auto","created_at":"2024-08-09 03:38:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1002335,"visible":true,"origin":"","legend":"","description":"","filename":"Bioinformatics2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4248864/v1_covered_fe743290-02c5-4ea6-84e8-1aaeddbdf9c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disease prediction model: An efficient machine learning- based DNA classifier","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"DNA sequences, Classifiers, Feature extraction, Healthcare, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4248864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4248864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhen it comes to health care, everyone is always eager to identify diseases in their early stages, but doing so might be difficult because of the lack of knowledge on the patterns of specific diseases since DNA contains most of the genetic blueprints, DNA sequence classification can be used to predict the existence of certain conditions accurately. There are several machine-learning techniques available to classify DNA sequences. Traits from known diseases are extracted to train the model for new, unknown diseases. The expansion of patients' access to digital platforms for early disease diagnosis through knowledge transfer to artificial neural networks eliminates the need for clinical equipment. To analyze the model, DNA samples of four well-known viruses\u0026mdash;human respiratory viruses, lung cancer viruses, and papilla-maviruses (HPV)\u0026mdash;are gathered from Genbank (NCBI). These samples are then compared with five existing methods using seven different parameters\u0026mdash;specificity, accuracy, Matthews correlation coefficient, recall, precision, F1-score, area under the receiver operating characteristic (ROC) curve (AUROC), and area under the Precision-Recall (PRC) curve (AUPRC)\u0026mdash;to facilitate the analysis of the model. 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