HEART: Hierarchical ensemble model using augmented representations and tabular learning for coronary artery disease prediction

HEART: Hierarchical ensemble model using augmented representations and tabular learning for coronary artery disease prediction | 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 HEART: Hierarchical ensemble model using augmented representations and tabular learning for coronary artery disease prediction Dimitrios Papakyriakopoulos, Pantelis Z. Lappas, Manolis N. Kritikos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8239358/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 Coronary Artery Disease (CAD) remains one of the most widespread and life-threatening cardiovascular diseases, ranking among the leading causes of mortality around the world. The high prevalence of CAD highlights the urgent need for effective early detection methods, but its diagnosis often relies on invasive or imperfect screening tools that delay intervention and increase risk. To address this challenge, we introduce HEART, a novel machine learning framework that combines structured clinical knowledge with advanced ensemble learning and data-centric augmentation to enhance early CAD prediction. HEART is a two-level ensemble model, where nine diverse models act as base learners. These include Logistic Regression, Elastic Net Regression, Support Vector Machine, K-Nearest Neighbors, Radius Neighbors, Extra Trees, LightGBM, TabNet and TabPFN. Their predictions are combined by a TabPFN meta-learner that captures complex interactions among model outputs. We use Mutual Information (MI) for feature selection and to address class imbalance and limited data, we use a hybrid augmentation strategy that combines synthetic minority oversampling technique (SMOTE) with class-specific Autoencoder reconstructions. Our study, evaluated in the Sani Z-Alizadeh dataset, increases the dataset to a final set of 1,000 samples and demonstrates that HEART achieves a top accuracy of 91% under fully nested stratified ten-fold cross-validation compared to the other nine distinct classifiers. Coronary Artery Disease Ensemble Learning Mutual Information Machine Learning Healthcare Full Text 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-8239358","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594064183,"identity":"bd91ba79-e1cd-4aa3-ba96-e6a5e96ead3b","order_by":0,"name":"Dimitrios Papakyriakopoulos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDCCA+yHH/+psGFgYGdgYGYwAAklENLCk2bAcyYNpJ5oLQwGErxth6FaGIjQwne7IcFA4sx5eX5mHtPNBQV2DPzsOQZ4tUjeOXjggUHFbcOZzTxmt2cYJDNI9rzBr8XgRkKCQcKZ2wkGh4FaeAyA3rlBwBagFgOJg23nYFrqGeyJ0SLZ2HYApuUwMDQI+uVMmjHDmWSgX9jKgH45ziNx5lkBXi18t9sPP2aosJPnZ2/edrvgT7Ucf3vyBrxaGCTQ+Dz4lWPTMgpGwSgYBaMAAwAAVxBIQ13pWTkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0002-5262-0669","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Dimitrios","middleName":"","lastName":"Papakyriakopoulos","suffix":""},{"id":594064184,"identity":"280c562f-46dd-48ff-aeac-0a3c6acf5c61","order_by":1,"name":"Pantelis Z. 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The high prevalence of CAD highlights the urgent need for effective early detection methods, but its diagnosis often relies on invasive or imperfect screening tools that delay intervention and increase risk. To address this challenge, we introduce HEART, a novel machine learning framework that combines structured clinical knowledge with advanced ensemble learning and data-centric augmentation to enhance early CAD prediction. HEART is a two-level ensemble model, where nine diverse models act as base learners. These include Logistic Regression, Elastic Net Regression, Support Vector Machine, K-Nearest Neighbors, Radius Neighbors, Extra Trees, LightGBM, TabNet and TabPFN. Their predictions are combined by a TabPFN meta-learner that captures complex interactions among model outputs. We use Mutual Information (MI) for feature selection and to address class imbalance and limited data, we use a hybrid augmentation strategy that combines synthetic minority oversampling technique (SMOTE) with class-specific Autoencoder reconstructions. 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