[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118257-en":3,"doc-seo-118257-105":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},118257,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Heart Attack Analysis and Prediction with MachineLearning Techniques","Heart attack is a major global health threat that can cause death or lasting disability. Accurate identification of risk and early warning signals is essential for timely preventive action and improved quality of life. This investigation uses machine learning on cardiovascular data to analyze and predict heart attack risk using genetics, lifestyle, medical history, and biometric factors drawn from clinical sources. Visualization and multiple models support the evaluation of predictive performance and key risk drivers.","Content list available at JournalPark  \nTurkish Journal of Forecasting  \nJournal Homepage: [tjforecasting.com](tjforecasting.com)  \nHeart Attack Analysis and Prediction with MachineLearning Techniques  \nShuaib Ayad Jasim Jasim􀀍 , Dr. Öğr. Üyesi İbrahaim Onaran2, Dr. Mustafa Al-Asadi,3  \n1 Department of Computer Engineering, KTO Karatay University: Baghdad / Iraq  \n2 Department of Computer Engineering, KTO Karatay University: Konya, TR 3Department of Computer Engineering, KTO Karatay University: Konya, Baghdad / Iraq  \nAbstract  \nHeart attack is an important health problemworldwide, and it is a situation that can lead to fatal consequences or permanent health problems. Identifying the risk of heart attacks and recognizing early warning signals is crucial to improving people's quality of life and implementing preventive measures. This investigation studies the analysis and prediction of heart attacks. The strategies rely on applying machine learning algorithms to cardiovascular data. Applying machine learning algorithms to cardiovascular data. The dataset includes genetics, lifestyle, medical history, and biometric factors associated with heart attack risk, collected froma variety of clinical sources.  \nDuring the analysis process, the data set was examined in detail with visualization processes and a series of models were created using different machine learning techniques. Models include logistic regression, support vector machines, decision trees, and random forests. A model is trained, tested, and tested on a set of data. According to studies, support vector machines are the most effective model for predicting the risk of heart attacks.  \nThis model stands out with its high accuracy rate and low error rate. In addition, important factors identified during the analysis process are also presented. These factors include various risk factors such as chest pain, fasting blood sugar, cholesterol level and blood pressure.  \nThe results of this study show that machine learningtechniques can be an effective tool in heart attack analysis and prediction. This objective quantitative method can assist healthcare professionals and individuals in the process ofdetermining individuals'heart attack risk and taking preventive measures. In addition, it is thought that this method can be improved and made more useful in heart attack managementwith more research and data collection.  \nKeywords: machine learning; heart attack; prediction; Python  \n􀀍 Corresponding Author.  \nE-mail [addresses : shuaibayad2@gmail.com](addresses : shuaibayad2@gmail.com) ( shuaib JASIM ) [onaran@gmail.com](onaran@gmail.com) (İbrahim ONARAN ) [mustafa.aadel@au.edu.iq](mustafa.aadel@au.edu.iq) (Mustafa AL-ASADİ)  \nORCID ID:  \nFirst Author’s Name: 0009-0000-5574-7302 Second Author’s Name: 0000-0002-7769-4077 Third Author’s Name: 0000-0002-8218-3458  \n1. Introduction  \nHeart attacks and other cardiovascular illnesses continue to be the world's top cause of death. The World Health Organization (WHO) estimates that cardiovascular illnesses cause 17.9 million deaths yearly, or 31% of all fatalities worldwide [1] .  \nEarly identification and prediction of heart attacks are crucial for timely medical intervention and Prevention of fatal outcomes. Machine learning algorithms have been extremely useful for forecasting the risks of disease and analysing large amounts of medical data. With the goal of advancing preventative healthcare, we explore the use of machine learning algorithms for heart attack analysis and prediction in this study.  \nThe urgent need to create precise and effective techniques for heart attack analysis and prediction is what inspired this study. Conventional methods of evaluating risk frequently depend on clinical variables including age, gender, blood pressure, cholesterol, and smoking status. Although these variables offer insightful information, they fall short of capturing the complexity of the underlying illness mechanisms, Machine learning algorithms o","cbCaigxkK6EXUpg6","https://ap.wps.com/l/cbCaigxkK6EXUpg6","pdf",428717,1,12,"English","en",105,"# Introduction\n## Research Motivation\n## Objectives and Scope\n# Methods and Models\n## Dataset and Visualization\n## Model Training and Testing\n## Evaluated Algorithms\n# Results and Risk Factors\n## Prediction Effectiveness\n## Identified Influential Variables\n# Conclusion and Future Work","[{\"question\":\"Why is early heart attack prediction important?\",\"answer\":\"Early identification enables timely medical intervention and prevention of fatal outcomes, improving patient quality of life and care decisions.\"},{\"question\":\"Which data types are used to predict heart attack risk?\",\"answer\":\"The study uses cardiovascular data including genetics, lifestyle, medical history, and biometric factors associated with heart attack risk.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"Logistic regression, support vector machines, decision trees, and random forests are used. The text reports support vector machines as the most effective for risk prediction.\"}]","Heart Attack Analysis and Prediction with MachineLearning Techniques | PDF",1785682683,30,{"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},"heart-attack-analysis-and-prediction-with-machinelearning-techniques","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/heart-attack-analysis-and-prediction-with-machinelearning-techniques/118257/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early heart attack prediction important?","Question",{"text":75,"@type":76},"Early identification enables timely medical intervention and prevention of fatal outcomes, improving patient quality of life and care decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data types are used to predict heart attack risk?",{"text":80,"@type":76},"The study uses cardiovascular data including genetics, lifestyle, medical history, and biometric factors associated with heart attack risk.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are compared in the study?",{"text":84,"@type":76},"Logistic regression, support vector machines, decision trees, and random forests are used. 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