[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122991-en":3,"doc-seo-122991-105":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":20,"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},122991,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","The Application of Machine Learning to the Prediction of Heart Attack","Heart illnesses contribute significantly to global mortality, with heart attacks accounting for a death every 33 seconds. To forecast heart disease events, a supervised machine learning approach is used, addressing the rising incidence of heart attacks in younger people. The goal is an early-warning system that detects risk from basic patient symptoms such as age, sex, and pulse rate. The proposed pipeline relies on neural-network-based machine learning algorithms to provide convenient and accurate risk prediction without frequent costly testing.","The Application of Machine Learning to the Prediction of Heart Attack  \nR. Regin*  \nAssistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and  \nTechnology, Ramapuram, [India. ](India. regin12006@yahoo.co.in)[regin12006@yahoo.co.in](India. regin12006@yahoo.co.in)  \nS. Suman Rajest  \nProfessor, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India.  \nShynu T  \nMaster of Engineering, Department of Biomedical Engineering, Agni College of Technology, Chennai,  \nTamil Nadu, India.  \nSteffi. R  \nAssistant Professor, Department of Electronics and Communication, Vins Christian College of  \nEngineering, Tamil Nadu, India.  \n***  \n----- - ---------------------- ------ ---------------------------- ----------------------- ----- -- -------------------------------  \nAbstract: Heart illnesses are among the most significant contributors to mortality in the world in the modern era. Heart attacks are responsible for the death of one person every 33 seconds. disease of the cardiovascular system by disclosing the proportion of mortality all over the world that are caused by heart attacks. In order to forecast instances of heart disease, a supervised machine learning method is utilised. Because the incidence of heart strokes in younger people is growing at an alarming rate, we need to establish a method that can identify the warning signs of a heart attack at an early stage and stop the stroke before it occurs. Because it is impractical for the average person to often undertake expensive tests like the electrocardiogram (ECG), there is a need for a system that is convenient and, at the sametime, accurate in forecasting the likelihood of developing heart disease. Therefore, our plan is to create a programme that, given basic symptoms such as age, sex, pulse rate, etc., can determine whether or not a person is at risk for developing a cardiac condition. The machine learning algorithm neural networks that are used in the suggested system are the most accurate and dependable.  \nKeywords: Machine Learning, Prediction of Heart Attack, Electrocardiogram, Heart Disease, Algorithm Neural Networks  \nIntroduction  \nOne of the conditions that affects the most people is heart disease. This sickness is still fairly prevalent in today's society. In our search for a more accurate technique of prediction, we made use of a variety of characteristics that have a strong bearing on the heart condition in question, and we also made use of algorithms [1] . The  \nalgorithm known as Naive Bayes is applied to a dataset consisting of risk factors and the results are analysed. In addition to using decision trees and a variety of different algorithms, we used the aforementioned characteristics to make predictions about heart disease [2-6] . The findings have demonstrated that even when the dataset is the superior approach for prediction, we nevertheless utilised algorithms for the purpose of making predictions. The naive Bayes algorithm is applied to a dataset consisting of risk factors and the results are analysed [7-14] . We also employed decision trees and a variety of algorithms in our attempt to forecast heart disease based on the results of a tiny naive Bayes algorithm. When used to huge datasets, decision trees are capable of producing reliable findings [15-21]. The art of prediction through the application of machine learning methods is the primary focus. These days, machine learning is employed extensively in a wide variety of business applications such as e-commerce [22-26] . Our topic is about the prediction of heart disease by processing a patient's dataset and data of Patients for whom we need to forecast the risk of occurrence of heart disease. Prediction is one of the domains in which this machine learning is applied [27-35] .  \nThe practise of discovering fascinating hidden patterns inside vast databases is known as data mining [36] . It is possible to mine the heterogeneous data in the medical do","cbCaii0DDPLYXDH4","https://ap.wps.com/l/cbCaii0DDPLYXDH4","pdf",1053843,1,21,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Prediction methods for heart disease\n## Data mining in medical prognosis","[{\"question\":\"What problem does the study target?\",\"answer\":\"The study targets early prediction of heart attack/heart disease risk to help prevent events before they occur.\"},{\"question\":\"Why is machine learning used in this research?\",\"answer\":\"Machine learning is used to discover patterns in patient data and predict the likelihood of developing cardiac conditions using supervised learning.\"},{\"question\":\"What patient inputs are used for prediction?\",\"answer\":\"The approach uses basic symptoms and demographic/clinical factors such as age, sex, pulse rate, and related risk factors.\"}]","The Application of Machine Learning to the Prediction of Heart Attack | PDF",1785814057,53,{"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},"the-application-of-machine-learning-to-the-prediction-of-heart-attack","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-application-of-machine-learning-to-the-prediction-of-heart-attack/122991/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study target?","Question",{"text":75,"@type":76},"The study targets early prediction of heart attack/heart disease risk to help prevent events before they occur.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is machine learning used in this research?",{"text":80,"@type":76},"Machine learning is used to discover patterns in patient data and predict the likelihood of developing cardiac conditions using supervised learning.",{"name":82,"@type":73,"acceptedAnswer":83},"What patient inputs are used for prediction?",{"text":84,"@type":76},"The approach uses basic symptoms and demographic/clinical factors such as age, sex, pulse rate, and related risk factors.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]