[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125693-en":3,"doc-seo-125693-105":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},125693,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Machine Learning Based Improved Heart Disease Detection with Confidence","Predicting heart attacks remains one of the most difficult tasks in medicine, motivating automated early-warning approaches based on diagnostic data. This study applies machine learning models to the well-known Cleveland heart disease dataset, using multiple performance indicators to assess each model’s effectiveness. Support vector machine and random forest show particularly promising results. An improved prediction method for an embedded platform is proposed by combining the strengths of several classifiers, increasing detection confidence when more than one classifier indicates heart disease. Experiments conclude with directions for further improvement.","Paper—Machine Learning Based Improved Heart Disease Detection with Confidence  \nMachine Learning Based Improved Heart Disease Detection with Confidence  \n[https://doi.org/10.3991/ijoe.v19i08.37417](https://doi.org/10.3991/ijoe.v19i08.37417)  \nAnas Domyati, Qurban Memon(􀀍)  \nUAE University, AlAin, United Arab Emirates  \n[qurban.memon@uaeu.ac.ae](qurban.memon@uaeu.ac.ae)  \nAbstract—One of the hardest jobs in medicine is to predict when someone will have a heart attack. Given how challenging it is to anticipate heart attack, there is an urgent need to automate the prediction process using diagnostic data, and at the very least generate an early warning. This research makes a contribution by making it easier to diagnose cardiac problems using machine learning methods applied on the well-known Cleveland heart disease dataset. Several performance indicators are utilized to evaluate each model’s strength. It turns out that support vector machine and random forest produced some incredibly promising outcomes. An improved prediction of heart disease for an embedded platform is, thus, proposed, based on the computational complexity of each model and experimental results, where the advantages of several classifiers are accumulated. The approach suggests that, and only if, more than one of these classifiers detect heart disease, the detection of heart illness is possible with increased confidence. In the end, experimental findings are drawn to a conclusion, with potential future options for advancing this effort.  \nKeywords—robust heart attack detection, support vector machine,  \nrandom forest, machine learning  \n1 Introduction  \nCoronary arteries become occluded and narrowed, which leads to heart failure. Coronary arteries regulate the blood flow to the heart. The typical symptoms of coronary disease include swollen feet, body weakness, breathing difficulties, and fatigue, among others. In the beginning, conventional investigative techniques were used to identify cardiac sickness, but it was later found that they were difficult. Because of shortage of medical diagnostic tools and healthcare professionals, heart disease detection and its treatment are incredibly challenging in underdeveloped regions. In a study conducted at World Health Organization [1], 17.90 million people passed away in 2016 as a result of cardiovascular disease. This amount is responsible for almost one-third of all deaths worldwide.  \nThe findings of physical examinations, medical history of patients, and a physician’s review of any pertinent symptoms are the main components of traditional invasive procedures for detecting heart disease [2]. Among the common processes, angiography is considered as one of the most accurate methods to identify heart issues. On the other  \nPaper—Machine Learning Based Improved Heart Disease Detection with Confidence  \nhand, angiography has a number of disadvantages, including relatively higher cost and a wide spectrum of unfavorable effects. In order to safeguard the patient’s health, a precise and correct diagnosis of cardiac disease is seen to be absolutely necessary.  \n20% of patients at high risk for cardiovascular disease are underdiagnosed as a result of risk misclassification [3] . Common diagnostic methods rely on the knowledge and experiences of medical specialists, which increases the risk of errors, delays appropriate treatment, lengthens treatment times, and dramatically increases costs. Therefore, it is considered essential and crucial to get a precise diagnosis of cardiac disease in order to save the patient from suffering more harm.  \nThough machine learning techniques may be able to predict and categorize people with heart disease using a variety of variables using various machine learning models. However, hospitals all over the world have been said to be in need of an intelligent system that creates databases of such patients and can assist the doctor in forecasting the severity of the condition. The objective of the pre","cbCaiehgSXvGRWq7","https://ap.wps.com/l/cbCaiehgSXvGRWq7","pdf",1092384,1,14,"English","en",105,"# Introduction\n## Motivation and challenges in heart disease diagnosis\n## Limitations of conventional invasive procedures\n## Role of machine learning and study objective\n# Literature review\n## Machine learning approaches using the Cleveland dataset","[{\"question\":\"Why is heart disease prediction considered challenging in medicine?\",\"answer\":\"Heart attack timing is difficult to anticipate using conventional diagnostic approaches, and reliable prediction requires diagnostic data and early warning systems to support clinicians.\"},{\"question\":\"Which machine learning models produced promising results in the study?\",\"answer\":\"The study reports that support vector machine and random forest achieved particularly promising outcomes on the Cleveland heart disease dataset.\"},{\"question\":\"How does the proposed approach increase confidence in heart disease detection?\",\"answer\":\"It combines multiple classifiers and suggests heart disease detection only when more than one of these classifiers indicates the presence of heart illness, thereby improving confidence.\"}]","Machine Learning Based Improved Heart Disease Detection with Confidence | 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is heart disease prediction considered challenging in medicine?","Question",{"text":75,"@type":76},"Heart attack timing is difficult to anticipate using conventional diagnostic approaches, and reliable prediction requires diagnostic data and early warning systems to support clinicians.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models produced promising results in the study?",{"text":80,"@type":76},"The study reports that support vector machine and random forest achieved particularly promising outcomes on the Cleveland heart disease dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach increase confidence in heart disease detection?",{"text":84,"@type":76},"It combines multiple classifiers and suggests heart disease detection only when more than one of these classifiers indicates the presence of heart illness, thereby improving 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