Artificial Intelligence and Big Data Analytics for Early Disease Prediction in Healthcare Systems

Artificial Intelligence and Big Data Analytics for Early Disease Prediction in Healthcare Systems | 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 Artificial Intelligence and Big Data Analytics for Early Disease Prediction in Healthcare Systems Ayomide Owolabi, Ahmed Bin Osman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8871492/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 The increasing burden of late-stage disease diagnosis remains a major challenge for global healthcare systems, despite the growing availability of large-scale clinical and population health data. Delayed detection of chronic and life-threatening conditions continues to drive higher mortality rates, treatment costs, and resource strain, highlighting the need for more proactive and data-driven diagnostic strategies. This study synthesizes recent advancements in artificial intelligence (AI) and big data analytics applied to early disease prediction across diverse healthcare settings. Drawing on peer-reviewed literature, the paper reviews machine learning, deep learning, and hybrid data-driven models used to analyze electronic health records, medical imaging, genomic data, and real-time patient monitoring systems. The findings indicate that AI-driven predictive analytics significantly enhance early detection accuracy, particularly for cardiovascular diseases, diabetes, cancer, and other chronic conditions. Several studies report improved sensitivity, risk stratification, and clinical decision support compared to traditional diagnostic approaches. However, despite these promising outcomes, widespread clinical adoption remains constrained by persistent challenges. These include data quality and bias, limited model generalizability across populations, ethical and regulatory concerns, and difficulties integrating AI tools into existing healthcare workflows. Overall, while AI and big data technologies demonstrate substantial potential to transform early disease prediction, addressing these limitations is critical for achieving reliable, scalable, and equitable real-world deployment. Artificial Intelligence and Machine Learning Artificial Intelligence Big Data Analytics Early Disease Prediction Predictive Healthcare Machine Learning Smart Healthcare Systems 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-8871492","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590876782,"identity":"fb1c7d2f-92cf-40da-8cce-2512c3fa72ca","order_by":0,"name":"Ayomide Owolabi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLCCBDYQmdjA8AFIsbGTooVxBkgLM1HWgLUkMDDzgGhCWvhnJB/d8KDMJpqfPbnxs82vbfJ8zAyMHz7m4NYicSMt7UbCubTcmT0Pm6Vz+24btjEzMEvO3IbHmhs5ZjcS2w7nbgCSzLk9txmBWtiYefFokYdo+Z+7H6TFsue2PUEtBhAtB3I3SAC1MPy4nUhQi+GZZyC/JOfOOPOwWbK34XZyGzNjM16/yB1PPnbzR5ldbn97+sMPP/7ctp3f3nzww0d83hdIQOIwtoHJBjzqgYD/ADLvD37Fo2AUjIJRMDIBABtAWUqsAw1LAAAAAElFTkSuQmCC","orcid":"","institution":"Ladoke Akintola University Of Technology Ogbomosho","correspondingAuthor":true,"prefix":"","firstName":"Ayomide","middleName":"","lastName":"Owolabi","suffix":""},{"id":590876783,"identity":"4f425386-e1d1-4407-bc6d-3dc9842411d0","order_by":1,"name":"Ahmed Bin Osman","email":"","orcid":"","institution":"Campbellsville University, Kentucky","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Bin","lastName":"Osman","suffix":""}],"badges":[],"createdAt":"2026-02-13 11:49:29","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8871492/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8871492/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102749194,"identity":"b98d59b6-e123-4f38-9967-1d7b6fbc4827","added_by":"auto","created_at":"2026-02-16 09:12:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":397918,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptABO3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8871492/v1_covered_bf6b5a6b-7cf3-4089-9520-e4a88314e6b4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eArtificial Intelligence and Big Data Analytics for Early Disease Prediction in Healthcare Systems\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Campbellsville University, Kentucky","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":"Artificial Intelligence, Big Data Analytics, Early Disease Prediction, Predictive Healthcare, Machine Learning, Smart Healthcare Systems","lastPublishedDoi":"10.21203/rs.3.rs-8871492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8871492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing burden of late-stage disease diagnosis remains a major challenge for global healthcare systems, despite the growing availability of large-scale clinical and population health data. 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