[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119887-en":3,"doc-seo-119887-105":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},119887,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction","Cardiovascular disease ranks among the leading causes of death worldwide, and heart-disease diagnosis is often complex, multi-step, and time-consuming. Advances in medical technology and research have improved diagnostic approaches, while machine learning offers data-driven assistance. This paper presents a diagnosis support system using optimized ML models—ANN, SVM, KNN, Naive Bayes, and Decision Tree—to analyze key cardiovascular risk factors. Using a dataset of 558 atherosclerosis patients, the system attains 96.67% accuracy for prediction, supporting earlier intervention and better patient outcomes.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 19 No. 15 (2023) |   \n[https://doi.org/10.3991/ijoe.v19i15.42653](https://doi.org/10.3991/ijoe.v19i15.42653)  \nPAPER  \nOptimizing Machine Learning Algorithms for Heart Disease Classification and Prediction  \nAbdeljalil El-Ibrahimi1, Oumaima Terrada1, Oussama El Gannour1, Bouchaib Cherradi1,2(􀀍), Ahmed El Abbassi3, Omar Bouattane1  \n1EEIS Laboratory, ENSET of Mohammedia, Hassan II University of Casablanca, Mohammedia, Morocco  \n2STIE Team, CRMEFCasablanca-Settat, Provincial Section of El Jadida, El Jadida, Morocco  \n3ERTTI Laboratory, FST of Errachidia, My Ismail University, Errachidia, Morocco  \nbouchaib.cherradi@ [enset-media.ac.ma](enset-media.ac.ma)  \nABSTRACT  \nAccording to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN.  \nKEYWORDS  \nmachine learning, atherosclerosis, cardiovascular disease, optimization  \n1 INTRODUCTION  \nAtherosclerosis, which is a type of heart disease, is largely caused by the accumulation of cholesterol deposits, called plaques, on the walls of the arteries. These plaques can restrict or block the flow of blood, leading to a range of serious health problems [1] . When the plaques build up in the coronary arteries, which supply blood to the heart muscle, it can cause angina (chest pain) or a heart attack. When plaques build up in the arteries that supply blood to the brain, it can cause a stroke. When plaques build up in the peripheral arteries, it can cause Peripheral Arterial Disease (PAD), which can lead to pain, numbness, and even amputation of the  \nEl-Ibrahimi, A., Terrada, O., Gannour, O. E., Cherradi, B., Abbassi, A. E., Bouattane, O. (2023) . Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction.InternationalJournal of Online and Biomedical Engineering (iJOE), 19(15), pp. 61–76.  [https://doi.org/10.3991/ijoe.v19i15.42653](https://doi.org/10.3991/ijoe.v19i15.42653)[ ](https://doi.org/10.3991/ijoe.v19i15.42653)[Article submitted 2023-06-28. Revision uploaded 2023-08-06. Final acceptance 2023-08-23.](Article submitted 2023-06-28. Revision uploaded 2023-08-06. Final acceptance 2023-08-23.)  \n© 2023 by the authors of this article. Published under CC-BY.  \niJOE | Vol. 19 No. 15 (2023) International Journal of Online and Biomedical Engineering (iJOE) 61  \nEl-Ibrahimi et al.  \naffected limb. Atherosclerosis is a chronic disease that develops over many years, and it is one of the leading causes of death globally [2] . The prevention of cardiovascular disease has become a major human health challenge in recent years. Early treatment of risk factors will reduce car","cbCaih06uiqjusEb","https://ap.wps.com/l/cbCaih06uiqjusEb","pdf",922346,1,16,"English","en",105,"# Introduction\n## Motivation and problem background\n## Role of AI and machine learning in diagnosis\n## Research aim and contributions\n# Methods and proposed diagnostic system\n## Optimized ML models for classification and prediction\n## Dataset description and risk factors\n# Results\n## Atherosclerosis prediction performance","[{\"question\":\"Why is heart disease diagnosis considered challenging?\",\"answer\":\"Diagnosis often requires combining clinical examinations, laboratory tests, and other procedures, making it complex and time-consuming. Manual extraction from unstructured records can also introduce errors and inconsistencies.\"},{\"question\":\"Which machine learning algorithms are used in the proposed system?\",\"answer\":\"The system uses optimized Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT).\"},{\"question\":\"What dataset and performance result are reported for atherosclerosis prediction?\",\"answer\":\"A medical dataset of 558 patients with atherosclerosis is used to train and validate the models. The approach reports 96.67% accuracy using ANN for atherosclerosis prediction.\"}]","Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction | PDF",1785726838,40,{"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},"optimizing-machine-learning-algorithms-for-heart-disease-classification-and-prediction","",{"@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/optimizing-machine-learning-algorithms-for-heart-disease-classification-and-prediction/119887/",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","2026-08-03",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},"Why is heart disease diagnosis considered challenging?","Question",{"text":75,"@type":76},"Diagnosis often requires combining clinical examinations, laboratory tests, and other procedures, making it complex and time-consuming. Manual extraction from unstructured records can also introduce errors and inconsistencies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used in the proposed system?",{"text":80,"@type":76},"The system uses optimized Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT).",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and performance result are reported for atherosclerosis prediction?",{"text":84,"@type":76},"A medical dataset of 558 patients with atherosclerosis is used to train and validate the models. 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