{"paper_id":"496d9e45-a6df-4e8e-a350-c8bb901925d9","body_text":"Multimodal Ensemble Learning for Coronary Artery Disease Risk Stratification Using ECG and Clinical Biomarkers | 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 Multimodal Ensemble Learning for Coronary Artery Disease Risk Stratification Using ECG and Clinical Biomarkers Vignesh Kumar Kaipa, Mohammed Bilal Makandar, Bazilla Wani, Bhumika Mandolkar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8832109/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 leading causes of mortality worldwide, necessitating accurate and early risk prediction to support timely clinical decision-making. Recent advances in artificial intelligence (AI) have demonstrated promising results in CAD detection using either electrocardiogram (ECG) signals or clinical biomarkers; however, single-modality approaches often fail to capture the complex and multifactorial nature of cardiovascular disease. In this paper, we propose a hybrid ensemble-based AI framework for CAD risk prediction that integrates ECG-derived features with lipid profile parameters to improve predictive performance and interpretability. Separate machine learning models are trained for each modality, and their outputs are combined using an ensemble learning strategy to generate a unified risk score. To enhance clinical transparency, explainable AI techniques are incorporated to identify the contribution of individual features toward model predictions. The proposed framework is evaluated using publicly available datasets, and experimental results demonstrate improved accuracy, robustness, and generalizability compared to standalone modality-specific models. The developed system highlights the potential of multimodal data fusion and explainable ensemble learning for reliable CAD risk stratification and supports its applicability in real-world clinical decision support systems. Cardiac & Cardiovascular Systems Artificial Intelligence and Machine Learning Coronary artery disease electrocardiogram lipid profile ensemble learning multimodal data fusion explainable artificial intelligence clinical decision support systems Full Text Additional Declarations The authors declare no competing interests. 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. 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