Long-term Heart Risk Prediction by Survival Analysis in Echocardiography: Leveraging Machine Learning, Interpretability Techniques, and Advanced Statistical Modelling

Long-term Heart Risk Prediction by Survival Analysis in Echocardiography: Leveraging Machine Learning, Interpretability Techniques, and Advanced Statistical Modelling | 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 Long-term Heart Risk Prediction by Survival Analysis in Echocardiography: Leveraging Machine Learning, Interpretability Techniques, and Advanced Statistical Modelling Md Abu Sufian, Wahiba Hamzi, Mai Ali, Amira Ali, Boumediene Hamzi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7981575/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background/Objectives: Survival analysis is critical for predicting time-to-event outcomes in cardiovascular care, such as patient survival following heart failure. This study leverages the UCI Echocardiogram dataset to enhance survival analysis by integrating Random Survival Forests (RSF) with Survshap (t) (Shapley Additive explanations for survival models), fractional polynomial modelling, and Bayesian methods. We addressed the limitations of traditional Cox models by capturing non-linear relationships, time-varying effects, and causal interactions. Methods: The dataset is a large population of 100000 samples, 17 variables, including 132 samples with variables such as age, wall motion index (WMI), and fractional shortening (FS), and was preprocessed to address missing values and outliers. RSF was applied to model complex interactions, achieving robust predictions of survival outcomes. Survshap (t) provided interpretability, identifying age and WMI as the most influential predictors. Fractional polynomial modelling captured non-linear relationships, enhancing the model’s adaptability—Bayesian survival analysis quantified uncertainty, and causal inference (propensity score matching) evaluated treatment effects. DeepHitSingle and validation metrics (Brier score and C-index) were used to assess robust performance. Results: The integrated approach demonstrated high predictive accuracy, achieving a Brier score of 0.141. Kaplan-Meier analysis indicated a survival probability of 75% at 10 months and approximately 60% at 40 months. The concordance index was 0.86. Random Survival Forest identified age (VIMP=10) and wall motion index (VIMP=20) as the top predictors, with SHAP analysis confirming their dynamic contributions, whereas Pericardial Effusion (PE) exhibited negligible predictive influence. Fractional polynomials effectively captured non-linear effects, such as age0.5 (HR = 1.03). Bayesian posterior estimates demonstrated reliability, with a baseline hazard of 2.036 (95% Highest Density Interval [1.83, 2.24]). Additionally, causal analysis revealed that smoking status had a minimal effect (ATE = 7.47 × 10−5). Conclusion: Combining RSF, interpretability techniques (Survshap (t), SHAP, LIME), and advanced statistical modelling (fractional polynomials, Bayesian methods) significantly improves survival analysis. The framework provides personalised risk stratification, validated through synthetic data and clinical decision-making, enabling early optimised intervention for high-risk groups and offering a transformative tool for echocardiography-based cardiovascular care for heart failure patients. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors Survival Analysis Random Survival Forest SurvSHAP(t) Interpretable AI Fractional Polynomials Bayesian Modelling Causal Inference UCI Echocardiogram Dataset Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 30 Oct, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 29 Oct, 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. 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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-7981575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542113862,"identity":"73f95325-4e91-4f29-bfe6-39f3a74777d8","order_by":0,"name":"Md Abu Sufian","email":"","orcid":"","institution":"University of East London","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Abu","lastName":"Sufian","suffix":""},{"id":542113863,"identity":"c2283e11-8b53-4463-a12f-0615a1c322d5","order_by":1,"name":"Wahiba Hamzi","email":"","orcid":"","institution":"University of 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Survival Forest, SurvSHAP(t), Interpretable AI, Fractional Polynomials, Bayesian Modelling, Causal Inference, UCI Echocardiogram Dataset","lastPublishedDoi":"10.21203/rs.3.rs-7981575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7981575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives:\u003c/strong\u003e Survival analysis is critical for predicting time-to-event outcomes in cardiovascular care, such as patient survival following heart failure. This study leverages the UCI Echocardiogram dataset to enhance survival analysis by integrating Random Survival Forests (RSF) with Survshap (t) (Shapley Additive explanations for survival models), fractional polynomial modelling, and Bayesian methods. We addressed the limitations of traditional Cox models by capturing non-linear relationships, time-varying effects, and causal interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The dataset is a large population of 100000 samples, 17 variables, including 132 samples with variables such as age, wall motion index (WMI), and fractional shortening (FS), and was preprocessed to address missing values and outliers. RSF was applied to model complex interactions, achieving robust predictions of survival outcomes. Survshap (t) provided interpretability, identifying age and WMI as the most influential predictors. Fractional polynomial modelling captured non-linear relationships, enhancing the model’s adaptability—Bayesian survival analysis quantified uncertainty, and causal inference (propensity score matching) evaluated treatment effects. DeepHitSingle and validation metrics (Brier score and C-index) were used to assess robust performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The integrated approach demonstrated high predictive accuracy, achieving a Brier score of 0.141. Kaplan-Meier analysis indicated a survival probability of 75% at 10 months and approximately 60% at 40 months. The concordance index was 0.86. Random Survival Forest identified age (VIMP=10) and wall motion index (VIMP=20) as the top predictors, with SHAP analysis confirming their dynamic contributions, whereas Pericardial Effusion (PE) exhibited negligible predictive influence. Fractional polynomials effectively captured non-linear effects, such as age0.5 (HR = 1.03). Bayesian posterior estimates demonstrated reliability, with a baseline hazard of 2.036 (95% Highest Density Interval [1.83, 2.24]). Additionally, causal analysis revealed that smoking status had a minimal effect (ATE = 7.47 × 10−5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eCombining RSF, interpretability techniques (Survshap (t), SHAP, LIME), and advanced statistical modelling (fractional polynomials, Bayesian methods) significantly improves survival analysis. The framework provides personalised risk stratification, validated through synthetic data and clinical decision-making, enabling early optimised intervention for high-risk groups and offering a transformative tool for echocardiography-based cardiovascular care for heart failure patients.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Long-term Heart Risk Prediction by Survival Analysis in Echocardiography: Leveraging Machine Learning, Interpretability Techniques, and Advanced Statistical Modelling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 11:28:24","doi":"10.21203/rs.3.rs-7981575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-04T02:45:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T06:17:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-30T06:16:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-29T15:30:31+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":"41198768-4fb1-42c9-8e9c-fa904005b8e3","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57672473,"name":"Health sciences/Cardiology"},{"id":57672474,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":57672475,"name":"Health sciences/Diseases"},{"id":57672476,"name":"Health sciences/Medical research"},{"id":57672477,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-13T11:28:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 11:28:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7981575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7981575","identity":"rs-7981575","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}