Machine Learning Prediction of Opioid Involvement in Connecticut Drug Overdose Deaths: Comparative Performance of Penalized Regression, Random Forest, and Support Vector Models

Machine Learning Prediction of Opioid Involvement in Connecticut Drug Overdose Deaths: Comparative Performance of Penalized Regression, Random Forest, and Support Vector Models | 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 Machine Learning Prediction of Opioid Involvement in Connecticut Drug Overdose Deaths: Comparative Performance of Penalized Regression, Random Forest, and Support Vector Models Fatih Ozkan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7546591/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 opioid epidemic remains a critical public health challenge in the United States, with Connecticut experiencing particularly high rates of opioid involvement in accidental drug overdose deaths. This study evaluates the predictive capacity of machine learning models—Ridge regression, LASSO regression, Random Forest, and Support Vector Machine (SVM)—to classify opioid involvement among 11,979 de-identified overdose cases recorded between 2012 and 2021. After rigorous preprocessing, stratified train–test splits, and hyperparameter optimization, model performance was assessed using accuracy, precision, recall, F1, and calibration metrics. All models demonstrated strong predictive utility, with Random Forest and SVM offering slightly better discrimination compared to penalized regressions, though at the cost of interpretability. LASSO identified key demographic and geographic predictors, including place of death and race/ethnicity, providing actionable insights for public health interventions. Findings highlight the potential of machine learning to enhance surveillance, risk stratification, and targeted resource allocation in combating the opioid crisis in Connecticut. Opioid crisis machine learning predictive modeling Ridge regression Random Forest Support Vector Machine Connecticut overdose data Full Text Additional Declarations No competing interests reported. Supplementary Files AccidentalDrugRelatedDeaths20122023.csv 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-7546591","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512123519,"identity":"aba326b2-2129-4593-b8c4-fd193f551acb","order_by":0,"name":"Fatih Ozkan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBAC9gYGhsMghgQQH/gI5PGBxdlwa+E5wAzXwnhwZgNMMQEtzFAtzId5idLCfv7g4QIGG3vJ9rMHDtvusEtsY+99wPCh7DBuLTzJDIdnMKQxS/PkJRzOPZOc2MZz3IBxxjncWuwZgFp4GA6zyTHkGBzObWNObJNIY2DmbcNjC/9jsBYeOf43Boct2+ohWv7i0yIBsUVCWgJoC2PbYYgWRrxaHhsAtaQZSM54Y3Cw98xx4zaeYwwHe86l43FY4uPPPMAQkzifY/zh545q2X72NsYHP8qscWoBA8Z/CLZjA5A4gF89GrAnSfUoGAWjYBSMCAAA7C1RM8Pqv94AAAAASUVORK5CYII=","orcid":"","institution":"Baylor University","correspondingAuthor":true,"prefix":"","firstName":"Fatih","middleName":"","lastName":"Ozkan","suffix":""}],"badges":[],"createdAt":"2025-09-05 18:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7546591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7546591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90850472,"identity":"a180d5b9-7012-4187-a072-e087ebf4f2db","added_by":"auto","created_at":"2025-09-09 03:00:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":939016,"visible":true,"origin":"","legend":"","description":"","filename":"MachineLearningPredictionofOpioidInvolvementinConnecticutDrugOverdoseDeathsComparativePerformanceofPenalizedRegressionRandomForestandSupportVectorModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7546591/v1_covered_6b761f70-a327-49a6-97e6-625b8d19025b.pdf"},{"id":90849721,"identity":"69ab0a0e-0f96-4968-a81b-33cffe007465","added_by":"auto","created_at":"2025-09-09 02:44:34","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4506120,"visible":true,"origin":"","legend":"","description":"","filename":"AccidentalDrugRelatedDeaths20122023.csv","url":"https://assets-eu.researchsquare.com/files/rs-7546591/v1/1fc81e0bd0cfc61f58abab5e.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Prediction of Opioid Involvement in Connecticut Drug Overdose Deaths: Comparative Performance of Penalized Regression, Random Forest, and Support Vector Models","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":"Opioid crisis, machine learning, predictive modeling, Ridge regression, Random Forest, Support Vector Machine, Connecticut overdose data","lastPublishedDoi":"10.21203/rs.3.rs-7546591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7546591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe opioid epidemic remains a critical public health challenge in the United States, with Connecticut experiencing particularly high rates of opioid involvement in accidental drug overdose deaths. This study evaluates the predictive capacity of machine learning models\u0026mdash;Ridge regression, LASSO regression, Random Forest, and Support Vector Machine (SVM)\u0026mdash;to classify opioid involvement among 11,979 de-identified overdose cases recorded between 2012 and 2021. After rigorous preprocessing, stratified train\u0026ndash;test splits, and hyperparameter optimization, model performance was assessed using accuracy, precision, recall, F1, and calibration metrics. All models demonstrated strong predictive utility, with Random Forest and SVM offering slightly better discrimination compared to penalized regressions, though at the cost of interpretability. LASSO identified key demographic and geographic predictors, including place of death and race/ethnicity, providing actionable insights for public health interventions. 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