[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125932-en":3,"doc-seo-125932-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125932,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Classification of Heart Diseases Based on Machine Learning: A Review","Early and accurate diagnosis of cardiovascular disease (CVD) is emphasized due to its significant global impact on mortality. Advances in machine learning (ML) are reviewed for classifying cardiac disorders to strengthen healthcare practice and improve management through more reliable detection. The review examines supervised methods including support vector machines, decision trees, and neural networks, along with their performance across varied datasets and pattern discovery. It also covers unsupervised clustering approaches, and discusses how ensemble learning and deep learning may raise classification accuracy. The work aims to guide policymakers, physicians, and researchers.","International Journal of Informatics, Information System and Computer Engineering  \nClassification of Heart Diseases Based on Machine  \nLearning: A Review Adnan Mohsin Abdulazeez*, Shereen Sadiq Hasan  \nDuhok Polytechnic University, Duhok, Iraq  \n*[Corresponding Email:](Corresponding Email: Adnan.mohsin@dpu.krd.edu)[ ](Corresponding Email: Adnan.mohsin@dpu.krd.edu)[Adnan.mohsin@dpu.krd.edu](Corresponding Email: Adnan.mohsin@dpu.krd.edu)  \nA B S T R A C T S  \nThe article emphasizes the critical need for early and accurate diagnosis of cardiovascular disease (CVD), a leading cause of global mortality. Recent advancementsin machine learning (ML) have shown promising results in classifying cardiac disorders, aiming to enhance healthcare practices. It discusses both the benefits and limitations of current ML algorithms used in this field, highlighting their role in improving the management of cardiac diseases through accurate diagnosis. The study evaluates various supervised learning techniques like support vector machines, decision trees, and neural networks, illustrating their effectiveness in handling diverse datasets and identifying significant patterns. Furthermore, it explores unsupervised learning methods such as clustering algorithms, which uncover hidden patterns in cardiac data. The research also investigates the potential of ensemble approaches and deep learning to further enhance classification accuracy. In conclusion, the study provides an overview of the current state of ML-based heart disease classification research, aiming to inform policymakers, physicians, and researchers about the transformative potential of ML in advancing heart disease diagnosis and treatment, ultimately aiming for improved patient outcomes and reduced healthcare costs.  \n© 2024 Tim Konferensi UNIKOM\u003C  \nA R T I C L E I N F O  \nArticle History:  \nReceived 14 Mar 2024 Revised 11 Apr 2024  \nAccepted 02 Jun 2024  \nAvailable online 07 Aug 2024 Publication date 01 June 2025  \nKeywords:  \nMachine learning algorithms, heart disease classification, cardiovascular disease.  \n1. INTRODUCTION  \nOne of the main causes of mortality worldwide is heart illnesses (CVD), also frequently known as cardiovascular disorders. There are a number of noteworthy cardiovascular diseases (CVDs), including congenital heart disease, rheumatic heart disease, peripheral artery disease, coronary heart disease, and cerebrovascular illness (Ahsan and Siddique, 2022) . The World Health Organization (WHO) estimates that heart disease and its aftereffects cause 17.9 million deaths worldwide. Heart attacks and strokes account for about 4 out of 5 fatalities related to CVD. Among the possible risk factors that hasten heart-related issues include poor eating habits, inactivity, alcoholism, and tobacco use. Consequently, the individual exhibits intermediate-risk indicators including high blood pressure, elevated blood sugar, excessive blood cholesterol, being overweight, and obesity (Kumar et al., 2023) . Unexpected and premature deaths can be avoided, nonetheless, by early detection of patients at high risk of CVD and provision of effective medications.  \nNumerous healthcare applications have found an efficient answer thanks to data mining (Kumar & Singh, 2018; Hassan et al., 2021; Ibrahim & Abdulazeez, 2021) such as patient deep representations (Zhang et al., 2018), medical image segmentation (Wang et al., 2018), and computer-aided detection (CAD) methods for diagnosing liver cancer (Ghoniem, 2020; Shin et al., 2016) and detection of Interstitial Lung Disease (ILD) (Ghoniem, 2020) . Since a prediction inaccuracy might have major consequences, the real medical dataset's complex nature necessitates careful  \nadministration (Ibrahim & Abdulazeez, 2021) . In order to precisely categorize the disease using machine learning algorithms and statistical methods, clinical informatics has been employed to analyze the EHR data. Because of this, algorithms for classification have been used in rece","cbCaiuk2m1ZjPYar","https://ap.wps.com/l/cbCaiuk2m1ZjPYar","pdf",735579,5,1,23,"English","en",105,"# Abstract\n# Introduction\n## Global burden of cardiovascular disease and risk factors\n## Role of data mining and clinical informatics\n## Machine learning methods in heart disease prediction","[{\"question\":\"Why is early diagnosis of cardiovascular disease important?\",\"answer\":\"Cardiovascular disease is a major global cause of death, and early detection of high-risk patients enables timely interventions and effective medication to avoid unexpected and premature deaths.\"},{\"question\":\"Which supervised learning techniques are reviewed for heart disease classification?\",\"answer\":\"The review discusses supervised learning approaches such as support vector machines, decision trees, and neural networks, highlighting their ability to handle diverse datasets and identify meaningful patterns.\"},{\"question\":\"What unsupervised methods and model strategies are considered to improve classification?\",\"answer\":\"Unsupervised learning methods such as clustering algorithms are used to uncover hidden structures in cardiac data, while ensemble approaches and deep learning are explored to further enhance classification accuracy.\"}]","Classification of Heart Diseases Based on Machine Learning: A Review | 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is early diagnosis of cardiovascular disease important?","Question",{"text":77,"@type":78},"Cardiovascular disease is a major global cause of death, and early detection of high-risk patients enables timely interventions and effective medication to avoid unexpected and premature deaths.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which supervised learning techniques are reviewed for heart disease classification?",{"text":82,"@type":78},"The review discusses supervised learning approaches such as support vector machines, decision trees, and neural networks, highlighting their ability to handle diverse datasets and identify meaningful patterns.",{"name":84,"@type":75,"acceptedAnswer":85},"What unsupervised methods and model strategies are considered to improve classification?",{"text":86,"@type":78},"Unsupervised learning methods such as clustering algorithms are used to uncover hidden structures in cardiac data, while ensemble approaches and deep learning are explored to 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