Multi-domain Identification of Myocardial Infarction Incidence using Explainable AI: The Overlooked Role of Periodontal Health

Multi-domain Identification of Myocardial Infarction Incidence using Explainable AI: The Overlooked Role of Periodontal Health | 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 Multi-domain Identification of Myocardial Infarction Incidence using Explainable AI: The Overlooked Role of Periodontal Health Moemen Hussein, Lu Li, Karen Falkner, Michael Buck, Patricia Diaz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8151863/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Myocardial infarction (MI) is a major global health concern influenced by diverse risk factors. Despite growing evidence of oral--systemic connections, current MI models largely exclude oral health indicators, reflecting the longstanding separation between dental and medical paradigms. This study introduces a multidomain, interpretable machine learning framework that integrates detailed periodontal and oral hygiene variables, marking one of the first efforts to quantitatively incorporate these features into MI incidence identification. A population-based case-control dataset comprising 1,355 individuals and heterogeneous variables was used to train and evaluate seven supervised classifiers via nested cross-validation. Among them, XGBoost achieved the best performance (AUC = 0.88 $\pm$ 0.01; F1 score = 0.74 $\pm$ 0.03) and was further probability-calibrated using isotonic regression, yielding a mean Brier score of 0.14 $\pm$ 0.01 and demonstrating well-aligned predicted probabilities. SHAP values confirmed the importance of conventional cardiovascular predictors, while several periodontal indicators such as mean clinical attachment loss, plaque index, and gingival bleeding emerged among the most influential features. Sex-stratified SHAP analysis revealed sex-specific patterns in the relative impact of oral features. Additionally, individual-level waterfall plots illustrated how oral inflammation may contribute independently or in combination with conventional factors to MI incidence identification. These findings support a systems-level view of periodontitis as a modifiable, biologically relevant factor in cardiovascular health and underscore the value of considering oral-health markers within screening and management frameworks. Health sciences/Biomarkers Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 19 Nov, 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. 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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-8151863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593549263,"identity":"3244cbfb-dfa7-4b34-bbfa-aab24670923d","order_by":0,"name":"Moemen Hussein","email":"","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Moemen","middleName":"","lastName":"Hussein","suffix":""},{"id":593549267,"identity":"03163e5f-60e7-4706-b81a-355a9963b617","order_by":1,"name":"Lu Li","email":"","orcid":"","institution":"University at Buffalo, State University of New 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Despite growing evidence of oral--systemic connections, current MI models largely exclude oral health indicators, reflecting the longstanding separation between dental and medical paradigms. This study introduces a multidomain, interpretable machine learning framework that integrates detailed periodontal and oral hygiene variables, marking one of the first efforts to quantitatively incorporate these features into MI incidence identification. A population-based case-control dataset comprising 1,355 individuals and heterogeneous variables was used to train and evaluate seven supervised classifiers via nested cross-validation. Among them, XGBoost achieved the best performance (AUC = 0.88 $\\pm$ 0.01; F1 score = 0.74 $\\pm$ 0.03) and was further probability-calibrated using isotonic regression, yielding a mean Brier score of 0.14 $\\pm$ 0.01 and demonstrating well-aligned predicted probabilities. SHAP values confirmed the importance of conventional cardiovascular predictors, while several periodontal indicators such as mean clinical attachment loss, plaque index, and gingival bleeding emerged among the most influential features. Sex-stratified SHAP analysis revealed sex-specific patterns in the relative impact of oral features. Additionally, individual-level waterfall plots illustrated how oral inflammation may contribute independently or in combination with conventional factors to MI incidence identification. 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