Efficient Automated Cardiovascular Disease Detection Using Machine Learning

Efficient Automated Cardiovascular Disease Detection Using Machine Learning | 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 Efficient Automated Cardiovascular Disease Detection Using Machine Learning Mohammad Karimi Moridani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4404419/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 Cardiovascular diseases pose a significant threat to global public health. Among diagnostic approaches, heart sound detection techniques offer non-invasive means for predicting cardiovascular conditions. While electrocardiogram (ECG) signals are commonly utilized for heart disease diagnosis, their limited spatial resolution necessitates alternative methods. Phonocardiogram (PCG) signals and sound processing techniques present viable alternatives. This paper explores the extraction of diverse features from PCG signals to classify patients using artificial intelligence algorithms. Simulation results demonstrate the superior performance of the XGBoost algorithm in cardiovascular patient detection compared to other methods. Specificity, sensitivity, and accuracy values were recorded at 99±1.93%, 98±2.76%, and 99±1.78%, respectively. Implementing the proposed method promises accurate and swift diagnosis of cardiovascular patients, offering significant support to healthcare professionals in patient screening. Future research avenues include extending this methodology to diagnose heart valve diseases, further enhancing its clinical utility. PCG Cardiovascular Patients Detection Feature Extraction XGBoost 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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