Attention-Driven Hybrid Vision Transformer Framework for Explainable Heart Disease Classification with Multi-Modal Unstructured Medical Data

Attention-Driven Hybrid Vision Transformer Framework for Explainable Heart Disease Classification with Multi-Modal Unstructured Medical Data | 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 Attention-Driven Hybrid Vision Transformer Framework for Explainable Heart Disease Classification with Multi-Modal Unstructured Medical Data Vudutha Sravanthi, Sheshikala Martha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8469724/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 Globally, heart disease remains a top health concern, often detected late and complex in its myriad risk factors. In today’s data-rich clinical environment, patient medical records constitute both structured and unstructured data. The traditional predictive modeling techniques often neglect this unstructured data, missing out on vital diagnostic clues. To address this, an Attention-Driven Hybrid Vision Transformer (ADHVT) framework is proposed that holistically combines multi-modal structured and unstructured data in heart disease classification. The framework incorporates Synthetic Minority Over-sampling TEchnique (SMOTE) to balance minority class distributions in both data forms and utilizes a hybrid molecule jump attention-based vision transformer with the Discrete Wavelet Transform (DWT) to extract spatial- and frequency-domain features. The classification is carried out through a convolutional GhostNet-based flashattention architecture integrated with an inception squeeze-and-excitation neural network. To enhance the performance of the framework, optimization is carried out through the groupers and moray eels algorithm. Explainability is provided through SHapley Additive exPlanations, allowing clinicians to justify the prediction. Experimental outcomes establish an outstanding average accuracy of 99.43%, specificity of 99.48%, recall of 99.38%, and error rate of 0.57 across multiple datasets, which proves the framework’s capability to utilize both structured and unstructured clinical data for making a dependable and interpretable prediction of heart disease. unstructured data optimization algorithm explainable artificial intelligence heart disease prediction deep learning structured data Full Text Additional Declarations No competing interests reported. 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-8469724","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566631118,"identity":"d75180bb-2aa3-48c8-b867-505bf3861f87","order_by":0,"name":"Vudutha 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In today\u0026rsquo;s data-rich clinical environment, patient medical records constitute both structured and unstructured data. The traditional predictive modeling techniques often neglect this unstructured data, missing out on vital diagnostic clues. To address this, an Attention-Driven Hybrid Vision Transformer (ADHVT) framework is proposed that holistically combines multi-modal structured and unstructured data in heart disease classification. The framework incorporates Synthetic Minority Over-sampling TEchnique (SMOTE) to balance minority class distributions in both data forms and utilizes a hybrid molecule jump attention-based vision transformer with the Discrete Wavelet Transform (DWT) to extract spatial- and frequency-domain features. The classification is carried out through a convolutional GhostNet-based flashattention architecture integrated with an inception squeeze-and-excitation neural network. 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