Explainable Artificial Intelligence for Predictive Toxicology and Public Health Risk Assessment: A Data-Driven Framework for Early Detection and Decision Support

Explainable Artificial Intelligence for Predictive Toxicology and Public Health Risk Assessment: A Data-Driven Framework for Early Detection and Decision Support | 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 Explainable Artificial Intelligence for Predictive Toxicology and Public Health Risk Assessment: A Data-Driven Framework for Early Detection and Decision Support Nnaemeka Kingsley Ugwumba, Peter Sunday Jaja, Juan Sebastian Murillejo Contreras, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9226177/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 Public health systems continue to face increasing challenges from environmental and chemical exposures that contribute to disease burden and population level risks. Traditional toxicological assessment methods are often limited by high cost, long experimental timelines, and difficulties in translating laboratory findings into real world decision making. This study presents a data driven framework that integrates explainable artificial intelligence techniques into predictive toxicology for improved public health risk assessment. The proposed approach combines machine learning models with interpretable mechanisms to support early detection of toxicological risks while maintaining transparency in model predictions. Multi source datasets including environmental exposure records, clinical health data, and chemical toxicity profiles are utilized to develop and validate the framework. The study demonstrates how interpretable predictive models can enhance risk classification accuracy and support evidence based public health interventions. Findings suggest that integrating explainability into predictive systems improves trust, usability, and policy relevance in toxicological applications. The framework contributes to advancing computational toxicology and offers practical implications for health agencies, researchers, and decision makers seeking timely and reliable risk assessment tools. Artificial Intelligence and Machine Learning Explainable models predictive toxicology public health risk assessment machine learning in health environmental exposure analysis interpretable systems decision support systems computational toxicology health data integration early risk detection 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-9226177","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612201024,"identity":"871541f4-0d93-409e-b923-f9d4e9d387c5","order_by":0,"name":"Nnaemeka Kingsley Ugwumba","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-2493-9846","institution":"Laskenta Technologies Limited","correspondingAuthor":true,"prefix":"","firstName":"Nnaemeka","middleName":"Kingsley","lastName":"Ugwumba","suffix":""},{"id":612201025,"identity":"f72df8b8-93d1-4e94-891f-0160bcceebb5","order_by":1,"name":"Peter Sunday Jaja","email":"data:image/png;base64,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","orcid":"","institution":"Laskenta Technologies Limited","correspondingAuthor":true,"prefix":"","firstName":"Peter","middleName":"Sunday","lastName":"Jaja","suffix":""},{"id":612201026,"identity":"92de83b4-98b6-48e8-8462-21a5dd992bb0","order_by":2,"name":"Juan Sebastian Murillejo Contreras","email":"data:image/png;base64,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","orcid":"","institution":"Laskenta Technologies Limited","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"Sebastian Murillejo","lastName":"Contreras","suffix":""},{"id":612201027,"identity":"b9e546c1-48b5-4f98-a144-3a3ea7392ba8","order_by":3,"name":"Kelechi Ernest Okechukwu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJUlEQVRIie3PMUvDQBTA8RcOzuW1txpSv8NBIKP9Ki2BTOkggigWWihkirv9FpkyXzg4l8SshQo2Cs4NRamDYFLcmkDcRO4/HAfv/eAOQKf7iyEAARjVF0MACKQgk0096HcgcCDMUC6vB7QrAXOROqf1pI0wlMnuIvDGUSqF2E6fBlwJ5+bdPx9QIMXL6piYd4FrLQN/HGXBKLlXb8hT4a3PYrd6GLVt/5jwHB3SC64nUY5c9uYS+UqotRmTiiC1Ggn7+CFsK79q8lwEl2Y8aydZSCviT6IsBGlUxJxLapSxbCVmqGwLH73ZMlU8CZVEBopYRvyAlDT/haFb7PDKtfupW2z2UzmkkJflZ3w7ZCeL4rWBNEfwcHZdrzP2v9nW6XS6/943LBZuBeaUc8kAAAAASUVORK5CYII=","orcid":"","institution":"Laskenta Technologies Limited","correspondingAuthor":true,"prefix":"","firstName":"Kelechi","middleName":"Ernest","lastName":"Okechukwu","suffix":""}],"badges":[],"createdAt":"2026-03-25 17:56:43","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9226177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9226177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566570,"identity":"0b5d78c0-816b-42ed-aaa8-fa707d3dcb25","added_by":"auto","created_at":"2026-03-27 12:56:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":498157,"visible":true,"origin":"","legend":"","description":"","filename":"ToxicologyandPublicHealth.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9226177/v1_covered_c20848b7-0fc3-4dfc-b2b8-8cccb4bea9d8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExplainable Artificial Intelligence for Predictive Toxicology and Public Health Risk Assessment: A Data-Driven Framework for Early Detection and Decision Support\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Laskenta Technologies Limited","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":"Explainable models, predictive toxicology, public health risk assessment, machine learning in health, environmental exposure analysis, interpretable systems, decision support systems, computational toxicology, health data integration, early risk detection","lastPublishedDoi":"10.21203/rs.3.rs-9226177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9226177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePublic health systems continue to face increasing challenges from environmental and chemical exposures that contribute to disease burden and population level risks. Traditional toxicological assessment methods are often limited by high cost, long experimental timelines, and difficulties in translating laboratory findings into real world decision making. This study presents a data driven framework that integrates explainable artificial intelligence techniques into predictive toxicology for improved public health risk assessment. The proposed approach combines machine learning models with interpretable mechanisms to support early detection of toxicological risks while maintaining transparency in model predictions. Multi source datasets including environmental exposure records, clinical health data, and chemical toxicity profiles are utilized to develop and validate the framework. The study demonstrates how interpretable predictive models can enhance risk classification accuracy and support evidence based public health interventions. Findings suggest that integrating explainability into predictive systems improves trust, usability, and policy relevance in toxicological applications. The framework contributes to advancing computational toxicology and offers practical implications for health agencies, researchers, and decision makers seeking timely and reliable risk assessment tools.\u003c/p\u003e","manuscriptTitle":"Explainable Artificial Intelligence for Predictive Toxicology and Public Health Risk Assessment: A Data-Driven Framework for Early Detection and Decision Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 03:59:31","doi":"10.21203/rs.3.rs-9226177/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"2ebaabe4-3c99-4688-8619-ac5d173b08b4","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65142805,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-03-27T03:59:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 03:59:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9226177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9226177","identity":"rs-9226177","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}