[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123126-en":3,"doc-seo-123126-105":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},123126,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Heart Attack Analysis Using Ensemble Machine Learning - Review Paper","Heart disease remains a leading cause of mortality worldwide, making accurate and timely diagnosis a critical need. This paper reviews machine-learning–based methods for heart disease detection, covering classical statistical approaches and modern deep learning. It discusses key risk factors such as hypertension, cholesterol levels, and lifestyle choices, and explains how ML has evolved in healthcare to improve diagnostic accuracy and patient care. Using large-scale datasets and feature engineering, traditional models like SVM and Random Forests help identify cardiac abnormalities, while CNN and RNN capture complex patterns and temporal dependencies.","Heart Attack Analysis Using Ensemble Machine  \nLearning  \nInternational Journal of Advanced Scientific Innovation Volume 06 Issue 05, May 2024  \nISSN: 2582-8436  \nDr. Raviprakash ML  \nProfessor  \nDept of CSE, KIT Tiptur  \nInchara H P[incharahp3008@gmail.com](incharahp3008@gmail.com)[ ](incharahp3008@gmail.com)Dept of CSE, KIT Tiptur  \nS Pallavi[pallavihaps123@gmail.com](pallavihaps123@gmail.com)[ ](pallavihaps123@gmail.com)Dept of CSE, KIT Tiptur  \nSheetal K M[ssheetalkm@gmail.com](ssheetalkm@gmail.com)[ ](ssheetalkm@gmail.com)Dept of CSE, KIT Tiptur  \nNitish N Kotumuchagi  \n[nitishnkotumuchagi@gmail.com](nitishnkotumuchagi@gmail.com)[ ](nitishnkotumuchagi@gmail.com)Dept of CSE, KIT Tiptur  \nAbstract—Heart disease remains one of the leading causes of mortality worldwide, emphasizing the urgent need for accurate and timely diagnosis. While traditional diagnostic methods have proven effective, advancements in machine learning (ML) offer promising avenues for enhanced detection and prevention strategies. This paper presents a comprehensive review of existing ML-based approaches for heart disease detection, ranging from classical statistical methods to cutting-edge deep learning techniques. We begin by outlining the various risk factors associated with heart disease, including hypertension, cholesterol levels, and lifestyle choices. Subsequently, we delve into the evolution of ML in healthcare, highlighting its transformative impact on diagnostic accuracy and patient care. Leveraging large-scale datasets and feature engineering, traditional ML algorithms such as Support Vector Machines (SVM) and Random Forests have demonstrated notable success in identifying cardiac abnormalities. However, recent breakthroughs in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have revolutionized heart disease detection by extracting intricate patterns and temporal dependencies from raw data sources.  \nI. INTRODUCTION  \nThe human body. It’s imperative for individuals to prioritize heart care, given its significance. Various diseases are interconnected with heart health, underscoring the necessity for predicting heart attacks. Currently, many patients succumb to heart attacks, often identified only in the advanced stages. This unfortunate trend stems from insufficient instrumentation to accurately predict heart attacks using efficient algorithms. Healthcare industries face challenges in accurately predicting heart attacks at an early stage. The complexity of health history statistics and the inconsistency of real-world physical data make this task difficult.[1]  \nResearchers are exerting considerable effort to develop a prototype capable of accurately predicting heart attacks in the early stages, yet they encounter challenges in creating a suitable model. Each proposed structure has its own set of strengths and weaknesses. Machine learning systems have been trained to comprehend and utilize data effectively, marking the intersection of both machine intelligence and tech-  \nnology. Machine learning, as explained, learns from regular patterns in data.  \nDifferent researchers have focused on reducing cardiovascular features and extracting nonlinear features using discriminant analysis. Fisher’s method was employed in the experiment to address overfitting issues and enhance training speed.  \nThis study aims to optimize machine learning models for predicting heart disease while addressing the challenge of overfitting, particularly within Logistic Regression. By drawing random samples from the complete dataset, overfitting issues can be mitigated effectively. Additionally, the model training is conducted on data samples sourced from the UCI Machine Learning repository. Consequently, the primary objective of this research is to enhance the accuracy of heart disease prediction.[2]  \nOver recent decades, research endeavors have underscored the critical role of data mining in augmenting clinical diagnosis,","cbCaioEMHHS2090C","https://ap.wps.com/l/cbCaioEMHHS2090C","pdf",473215,1,5,"English","en",105,"# Abstract\n# Introduction\n# Literature Survey","[{\"question\":\"What is the main objective of the paper on heart attack analysis?\",\"answer\":\"To review and optimize machine learning approaches for predicting heart disease and addressing challenges such as overfitting, with an emphasis on improving prediction accuracy.\"},{\"question\":\"Which risk factors are highlighted as important for heart disease prediction?\",\"answer\":\"The paper highlights hypertension, cholesterol levels, and lifestyle choices as key risk factors, along with other cardiovascular indicators.\"},{\"question\":\"How do traditional machine learning methods and deep learning approaches differ in detection?\",\"answer\":\"Traditional methods like SVM and Random Forests identify abnormalities using feature engineering, while deep learning models such as CNN and RNN extract intricate patterns and temporal dependencies from raw or sequential data.\"}]","Heart Attack Analysis Using Ensemble Machine Learning - Review Paper | PDF",1785814761,13,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"heart-attack-analysis-using-ensemble-machine-learning-review-paper","",{"@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/heart-attack-analysis-using-ensemble-machine-learning-review-paper/123126/",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","2026-08-04",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},"What is the main objective of the paper on heart attack analysis?","Question",{"text":75,"@type":76},"To review and optimize machine learning approaches for predicting heart disease and addressing challenges such as overfitting, with an emphasis on improving prediction accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which risk factors are highlighted as important for heart disease prediction?",{"text":80,"@type":76},"The paper highlights hypertension, cholesterol levels, and lifestyle choices as key risk factors, along with other cardiovascular indicators.",{"name":82,"@type":73,"acceptedAnswer":83},"How do traditional machine learning methods and deep learning approaches differ in detection?",{"text":84,"@type":76},"Traditional methods like SVM and Random Forests identify abnormalities using feature engineering, while deep learning models such as CNN and RNN extract intricate patterns and temporal dependencies from raw or sequential data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]