[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123437-en":3,"doc-seo-123437-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123437,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Prediction of Cardiovascular Disease Using Machine Learning & Deep Learning Techniques - Cardio","Cardiovascular disease remains a major global public health threat, with accurate risk prediction essential to prevent severe outcomes. This paper presents a machine learning and deep learning based approach for predicting cardiovascular disease using public health datasets. Multiple classifiers are explored, including K-Nearest Neighbor, Naive Bayes, Decision Tree, Support Vector Machine, XGBoost, Artificial Neural Networks, and Convolutional Neural Networks. Experimental results indicate Artificial Neural Networks achieve improved prediction accuracy.","20(2): S2: 243-248, 2025  \n[www.thebioscan.com](www.thebioscan.com)  \nPREDICTION OF CARDIOVASCULAR DISEASE USING MACHINE LEARNING & DEEP LEARNING TECHNIQUES  \nDr K Venkata Nagendra1, Associate Professor, Dept. ofCSE, SRKR Engineering College, Bhimavaram.  \nMr.G. Rajesh2, Assistant Professor, Dept. ofCSE, NBKR Institute of Science & Technology, Vidyanagar, Tirupathi DT, A.P Mr. Erdi Raju Dayakar3, Assistant Professor, Dept. ofCSE, Sree Venkateswara College of Engineering, Nellore, AP.  \nMr. J Jagadeswara Reddy4, Assistant Professor, Dept. ofCSE, Sai Rajeswari Institute of Technology, Proddatur.  \nMr. Putheti Nagaraja5, Assistant Professor, Dept. ofCSE, Sree Venkateswara College of Engineering, Nellore, AP.  \nMr.S. Sujith Kumar6, Assistant Professor, Dept. ofCSE, Sree Venkateswara College of Engineering, Nellore, AP.  \nDOI: 10.63001/tbs.2025.v20.i02.S2.pp243-248  \nKEYWORDS  \nCardiovascular Disease,  \nArtificial Intelligence Convolutional Neural Networks,  \nSupport Vector Machines,  \nMachine Learning, Deep Learning, Random Forest  \nReceived on:  \n16-02-2025 Accepted on:  \n15-03-2025 Published on 06-05-2025  \nABSTRACT  \nHealthcare is very important aspects of human life. Cardiovascular disease, also known as the coronary artery disease, is one of the many deadly infections that kill people in India and around the world. Accurate predictions can prevent heart disease, but incorrect predictions can be fatal. Therefore, here this paper describes a method for predicting cardiovascular disease that makes use of Machine Learning (ML) and Deep Learning (DL) . The K-Nearest Neighbor method (KNN), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), XGBoost (Extreme Gradient Boosting), Artificial Neutral Network (ANN), and Convolutional Neutral Network (CNN) are among the classifiers used in this paper. From Public Health Dataset required data is collected and focused on recognizing the best approach for predicting the disease in preliminary phase. This experiment end results show that the use of Artificial Neural Networks can be of much useful in prediction with better accuracy (95.7%) than compared to any other ML approaches.  \nINTRODUCTION  \nCardiovascular disease represents a significant global public health issue. The number of individuals affected by heart disease is rapidly increasing due to inadequate health literacy and unhealthy lifestyle choices. The heart, an essential organ in humans, plays a crucial role in circulating blood throughout the body, functioning akin to a pump with a resting rate of approximately 72 beats per minute [1] . Research from the World Health Organization (WHO) indicates that heart-related ailments result in millions of fatalities each year worldwide, ranking among the top causes of death alongside mental stress, occupational strain, and various other illnesses [2] . An electrocardiogram (ECG) is utilized to capture the heart's electrical activity, serving as a common, non-invasive procedure for assessing cardiac health and swiftly identifying potential issues.  \nHistorically, clinicians relied on auscultation to listen to heart sounds, a method that helps distinguish between normal and abnormal heart sounds. Its low equipment requirements, pain-  \nfree nature, and cost-effectiveness make it particularly suitable for cardiac assessments in smaller urgent care facilities, where the clinician's training is vital for accurately evaluating patients and identifying cardiac sounds [3] .  \nArrhythmia is another prevalent heart condition [4] [5] that can manifest in various forms. There are two primary classifications of arrhythmias based on heart rate: (1) tachycardia, which refers to a rapid heartbeat exceeding 100 beats per minute, and (2) bradycardia, characterized by a slow heartbeat of fewer than 60 beats per minute. Physicians typically recommend a standard ECG to assess the heart's rhythm [6] .  \nIn contemporary society, individuals are often preoccupied with their daily routines, leading ","cbCaidZiZfSqLtDv","https://ap.wps.com/l/cbCaidZiZfSqLtDv","pdf",627910,1,6,"English","en",105,"# Introduction\n## Cardiovascular disease and ECG background\n## Arrhythmia classification and common assessment\n# Research Methodology\n## Decision Tree\n## Random Forest","[{\"question\":\"Which methods are used to predict cardiovascular disease in the study?\",\"answer\":\"The paper evaluates KNN, Naive Bayes, Decision Tree, SVM, XGBoost, Artificial Neural Networks, and Convolutional Neural Networks for cardiovascular disease prediction.\"},{\"question\":\"How is the dataset used in the methodology?\",\"answer\":\"Data are collected from a public health dataset and used to evaluate which approach performs best for disease prediction in the preliminary phase.\"},{\"question\":\"What accuracy does the study report for the top-performing model?\",\"answer\":\"The results show that Artificial Neural Networks provide better prediction accuracy, reaching about 95.7%.\"}]","Prediction of Cardiovascular Disease Using Machine Learning & Deep Learning Techniques - Cardio | PDF",1785816474,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-cardiovascular-disease-using-machine-learning-deep-learning-techniques-cardio","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-cardiovascular-disease-using-machine-learning-deep-learning-techniques-cardio/123437/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which methods are used to predict cardiovascular disease in the study?","Question",{"text":75,"@type":76},"The paper evaluates KNN, Naive Bayes, Decision Tree, SVM, XGBoost, Artificial Neural Networks, and Convolutional Neural Networks for cardiovascular disease prediction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset used in the methodology?",{"text":80,"@type":76},"Data are collected from a public health dataset and used to evaluate which approach performs best for disease prediction in the preliminary phase.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy does the study report for the top-performing model?",{"text":84,"@type":76},"The results show that Artificial Neural Networks provide better prediction accuracy, reaching about 95.7%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]