A comparative study of machine learning and traditional survival models in predicting 5-year post‑ coronary artery bypass grafting mortality

A comparative study of machine learning and traditional survival models in predicting 5-year post‑ coronary artery bypass grafting mortality | 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 Article A comparative study of machine learning and traditional survival models in predicting 5-year post‑ coronary artery bypass grafting mortality Shayesteh Alinia, Ghodratollah Roshanaei, Leila Mahmoudi, Maliheh Safari, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7517926/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Introduction: Coronary artery disease (CAD) is a major public health issue worldwide and in Iran, contributing substantially to morbidity and mortality. For patients with symptomatic and advanced forms of the disease, coronary artery bypass grafting (CABG) remains a widely utilized and effective treatment strategy. This study aimed to investigate five-year post-CABG survival and identify key predictors of mortality using traditional and machine learning (ML) survival models. Methods This retrospective cohort study included 2,860 patients who underwent isolated CABG between 2016 and 2021 at Farshchian Cardiovascular Hospital in Hamadan, Iran. Kaplan–Meier analysis and log-rank tests were used for univariate survival analysis. To assess predictive performance, several statistical and ML models were applied, including Cox regression, Bayesian Neural Network (BNN) survival, random forest, bagging survival, support vector machines (SVMs), and parametric Weibull regression. Model performance was evaluated using accuracy, area under the curve (AUC), Brier score, and F1-score. Result A total of 2,860 participants were included in the study, of whom 4.9% died during the follow‑up period. The mean age at death was significantly higher compared to that of survivors (68 vs. 63 years, p < 0.001), with a greater proportion aged ≥ 76 years among the deceased (26.6% vs. 8.5%). Among all models, the BNN Survival model demonstrated the best overall predictive performance. This model showed the highest sensitivity at 0.90 (95% CI: 0.70–0.99), with a specificity of 0.61 (95% CI: 0.47–0.83), and an AUC of 0.77 (0.68–0.85). Variable importance analysis showed age as the most influential predictor (normalized importance close to 1.0), followed by residential area (~ 0.65). Conclusion Advanced ML techniques, especially BNN survival models, can greatly improve the accuracy of long-term mortality predictions after CABG compared to traditional methods. Using these models in clinical practice could help tailor risk assessments to each patient and improve post-surgery care. Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Coronary Artery Bypass Grafting Survival Prediction Machine Learning Bayesian Neural Network Retrospective Cohort Cardiovascular Surgery Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Apr, 2026 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 26 Dec, 2025 Reviewers invited by journal 26 Dec, 2025 Editor assigned by journal 23 Dec, 2025 Editor invited by journal 08 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 05 Sep, 2025 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-7517926","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":565966889,"identity":"18e21760-1db9-4168-ac4d-34db8be291a0","order_by":0,"name":"Shayesteh Alinia","email":"","orcid":"","institution":"Zanjan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shayesteh","middleName":"","lastName":"Alinia","suffix":""},{"id":565966890,"identity":"5ed39f91-c809-4335-a4e5-7c7cb0502592","order_by":1,"name":"Ghodratollah 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mortality","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary Artery Bypass Grafting, Survival Prediction, Machine Learning, Bayesian Neural Network, Retrospective Cohort, Cardiovascular Surgery","lastPublishedDoi":"10.21203/rs.3.rs-7517926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7517926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eCoronary artery disease (CAD) is a major public health issue worldwide and in Iran, contributing substantially to morbidity and mortality. For patients with symptomatic and advanced forms of the disease, coronary artery bypass grafting (CABG) remains a widely utilized and effective treatment strategy. This study aimed to investigate five-year post-CABG survival and identify key predictors of mortality using traditional and machine learning (ML) survival models.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included 2,860 patients who underwent isolated CABG between 2016 and 2021 at Farshchian Cardiovascular Hospital in Hamadan, Iran. Kaplan\u0026ndash;Meier analysis and log-rank tests were used for univariate survival analysis. To assess predictive performance, several statistical and ML models were applied, including Cox regression, Bayesian Neural Network (BNN) survival, random forest, bagging survival, support vector machines (SVMs), and parametric Weibull regression. Model performance was evaluated using accuracy, area under the curve (AUC), Brier score, and F1-score.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eA total of 2,860 participants were included in the study, of whom 4.9% died during the follow‑up period. The mean age at death was significantly higher compared to that of survivors (68 vs. 63 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a greater proportion aged\u0026thinsp;\u0026ge;\u0026thinsp;76 years among the deceased (26.6% vs. 8.5%). Among all models, the BNN Survival model demonstrated the best overall predictive performance. This model showed the highest sensitivity at 0.90 (95% CI: 0.70\u0026ndash;0.99), with a specificity of 0.61 (95% CI: 0.47\u0026ndash;0.83), and an AUC of 0.77 (0.68\u0026ndash;0.85). Variable importance analysis showed age as the most influential predictor (normalized importance close to 1.0), followed by residential area (~\u0026thinsp;0.65).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAdvanced ML techniques, especially BNN survival models, can greatly improve the accuracy of long-term mortality predictions after CABG compared to traditional methods. Using these models in clinical practice could help tailor risk assessments to each patient and improve post-surgery care.\u003c/p\u003e","manuscriptTitle":"A comparative study of machine learning and traditional survival models in predicting 5-year post‑ coronary artery bypass grafting mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 12:52:50","doi":"10.21203/rs.3.rs-7517926/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"112702907389807227569661872435249120039","date":"2026-04-27T14:21:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-26T19:08:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249677343968769916281543462958947938216","date":"2025-12-26T19:03:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-26T12:19:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-23T12:06:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T17:37:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T16:36:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-05T16:30:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f78fcf82-9a70-4336-9459-d7fd107ab60c","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":60252942,"name":"Health sciences/Cardiology"},{"id":60252943,"name":"Health sciences/Diseases"},{"id":60252944,"name":"Health sciences/Medical research"},{"id":60252945,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-29T12:52:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 12:52:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7517926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7517926","identity":"rs-7517926","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}