[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117098-en":3,"doc-seo-117098-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117098,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Prediction of Heart Disease Using Machine Learning Techniques","Heart disease prediction using machine learning supports early identification and improves prevention and treatment decisions in healthcare. Large-scale patient information can be processed to reveal patterns and risk factors behind cardiac disease onset. Supervised, unsupervised, and ensemble learning approaches analyze clinical test results, patient demographics, and medical records. Models are trained on historical datasets to generate accurate, computationally efficient risk predictions. Feature selection is emphasized to isolate significant risk factors, and methods such as logistic regression, SVM, decision trees, random forests, and neural networks are applied for effective prediction.","Prediction of Heart Disease Using Machine Learning  \nTechniques  \nMohammed Jasim A.Alkhafaji 1  \n1Computer Technology Engineering  \nAl_Taff university college  \nKarbala, Iraq  \nSamah A Nasir1,2  \n1Computer Technology Engineering, Al_Taff university college  \n2Ministry of Education, Karbala Directorate  \nKerbala, Iraq  \nZinah alhussein1  \n1Computer Technology Engineering  \nAl_Taff university college  \nKarbala, Iraq  \nAbstract—A potential strategy in the healthcare industry is the prediction of cardiac disease using machine learning algorithms. Worldwide, heart disease continues to be one of the major causes of death, and successful treatment and prevention depend greatly on early identification. Large volumes of patient data may be analyzed using machine learning algorithms to find patterns and risk factors that might lead to the onset of heart disease. These algorithms use supervised learning, unsupervised learning, and ensemble approaches to assess a variety of data sources, including clinical test results, patient demographics, and medical records. Machine-learning algorithms may be trained on historical data from a variety of patients to discover complicated associations and generate precise predictions about a person's risk of acquiring heart disease. Our objective is to create a machine-learning technique that reliably predicts heart disease and is computationally effective. Feature selection is a crucial step in the creation of prediction models as it permits the identification of the most significant risk factors for heart disease. Machine learning methods including logistic regression, support vector machines, decision trees, random forests, and neural networks are often used to predict cardiac disease. By examining extensive patient data, machine learning algorithms show considerable potential in the prediction of cardiac disease. In the battle against heart disease, their capacity to spot patterns and risk factors may result in early identification, individualized therapies, and better patient care.  \nKeywords-data mining, Prediction, heart disease, machine learning, technique.  \nI. INTRODUCTION  \nThe World Health Organization (WHO) has a crucial role to play in tackling the problem of heart disease prediction, which is a major area of concern for global health. The WHO is a specialized department of the UN that deals with global public health. It works to combat various diseases, including cardiovascular diseases like heart disease, through research, policy development, and global coordination efforts. The WHO recognizes heart disease as a major global health challenge and emphasizes the importance of prevention, early detection, and treatment. It collaborates with member countries to develop strategies and guidelines for heart disease prevention and control. The organization also promotes awareness campaigns to educate the public about risk factors and healthy lifestyle choices that can reduce the burden of heart disease [1] .  \nheart disease mortality, remains a leading cause of death worldwide. According to the WHO's latest global health estimates from 2020, cardiovascular diseases accounted for approximately 17.9 million deaths or 32% of all deaths  \nglobally. Among cardiovascular diseases, ischemic heart disease (caused by narrowed coronary arteries) is the leading cause, responsible for 8.9 million deaths, followed by stroke with 6.3 million deaths. The World Health Organization (WHO) recognizes the importance of various medical data in predicting heart disease. These data points provide valuable insights into an individual's risk factors and help in assessing the likelihood of developing cardiovascular conditions [2] . Information about previous cardiovascular events, such as heart attacks, strokes, or other heart-related conditions, helps assess an individual's predisposition to heart disease. The presence of known risk factors, including hypertension (high blood pressure), dyslipidemia (abnormal cholesterol","cbCaihuCdDPuATpQ","https://ap.wps.com/l/cbCaihuCdDPuATpQ","pdf",261771,1,5,"English","en",105,"# Introduction\n## Heart disease burden and WHO role\n## Medical data and risk factors for prediction\n# Literature survey","[{\"question\":\"Why is early prediction of heart disease important?\",\"answer\":\"Early identification is crucial because successful prevention and treatment depend on detecting risk before severe outcomes occur. Machine learning supports this by finding patterns linked to disease onset.\"},{\"question\":\"What kinds of data are used to predict heart disease?\",\"answer\":\"Prediction models use clinical test results, patient demographics, medical records, prior cardiovascular events, symptoms, and biomarkers such as lipid profiles and blood glucose.\"},{\"question\":\"Which machine learning methods are commonly used in heart disease prediction?\",\"answer\":\"Common approaches include logistic regression, support vector machines, decision trees, random forests, neural networks, and other classifiers referenced in related work.\"}]","Prediction of Heart Disease Using Machine Learning Techniques | PDF",1785673733,13,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"prediction-of-heart-disease-using-machine-learning-techniques","",{"@graph":36,"@context":86},[37,54,69],{"@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-heart-disease-using-machine-learning-techniques/117098/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early prediction of heart disease important?","Question",{"text":76,"@type":77},"Early identification is crucial because successful prevention and treatment depend on detecting risk before severe outcomes occur. Machine learning supports this by finding patterns linked to disease onset.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What kinds of data are used to predict heart disease?",{"text":81,"@type":77},"Prediction models use clinical test results, patient demographics, medical records, prior cardiovascular events, symptoms, and biomarkers such as lipid profiles and blood glucose.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning methods are commonly used in heart disease prediction?",{"text":85,"@type":77},"Common approaches include logistic regression, support vector machines, decision trees, random forests, neural networks, and other classifiers referenced in related work.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":21,"slug":138},19,"General","general"]