[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123527-en":3,"doc-seo-123527-105":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},123527,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",7,"Healthcare","Advancements In Heart Disease Prediction - A Machine Learning Approach For Early Detection And Risk Assessment - Abstract And Model Evaluation","Machine learning models are evaluated for anticipating heart disease risks using cross-sectional clinical data. The study analyzes how multiple clinical features contribute to separating patients with and without heart disease while constructing a reliable clinical dataset. Seven classifiers are explored—Logistic Regression, Random Forest, Decision Tree, Naive Bayes, k-nearest Neighbors, Neural Networks, and Support Vector Machine (SVM)—with performance assessed using accuracy metrics. SVM achieves the highest reported accuracy (91.51%), supporting advanced computational approaches for cardiovascular risk assessment, improvement, and management.","Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection  \nAnd Risk Assessment  \n1Balaji Shesharao Ingole, 2Vishnu Ramineni, 3Nikhil Bangad, 4Koushik Kumar Ganeeb,  \n5Priyankkumar Patel,  \n1Researcher, 2 Senior Staff Software Engineer, 3Data Engineer, 4Researcher, 5Researcher  \n1Indepedent Research,  \n1IEEE Org, Evans, GA, USA  \nAbstract: The primary aim of the paper is to comprehend, assess, and analyze the role, relevance, and efficiency of machine learning models in anticipating heart disease risks using clinical data. While the essentiality of heart disease risk prediction can’t be emphasized more, the usage of machine learning (ML) in the identification and assessment of the effect of its multiple features on the division of patients with and without heart disease, generating a reliable clinical dataset, is equally important. The paper relies essentially on cross-sectional clinical data. The ML approach is designed potentially to strengthen various clinical features in the heart disease prognosis process. Some features turn out to be strong predictors adding potential values. The paper entails seven ML classifiers Logistic Regression, Random Forest, Decision Tree, Naive Bayes, k-nearest Neighbors, Neural Networks, and Support Vector Machine (SVM) . The evaluation of the performance of each model is done based on accuracy metrics. Interestingly, the Support Vector Machine (SVM) demonstrates the highest accuracy percentage i.e. 91.51%, proving its worth among the evaluated models in the realm of predictive ability. The overall findings ofthe research demonstrate the superiority of advanced computational methodologies in the evaluation, prediction, improvement, and management of cardiovascular risks. In other words, the high potential of the SVM model exhibits its applicability and worth in clinical settings, leading the way to further progressions in personalized medicine and healthcare.  \nIndexTerms - Machine Learning, Heart Disease Prediction, Support Vector Machine, Clinical Data, Cardiovascular Risk Assessment, Predictive Modeling, Personalized Medicine.  \nI.INTRODUCTION  \nThe heart is one of the most vital components of the human body. Heart diseases have become a common phenomenon and are counted among the root causes of death at the global level. Therefore, it’s essential to develop efficient predictive models to detect heart diseases at an early stage and perform interventions on a timely basis. The fundamental objective of the research is to offer a prediction framework that can be beneficial to detect diseases, in general, and heart diseases in particular at an early stage; to carryout risk assessment and management; to provide extensive aid to the healthcare field to incorporate the progressive machine learning and data analytical tools for the betterment of humanity. Such integration of technological innovations in clinical practices can transform the entire cardiovascular healthcare facility. This can enhance the risk identification process and improve treatment strategies, proactively reducing the possibility of death due to minimal or negligible healthcare facilities. Besides, this can benefit in improving patient outcomes and their quality of life [18] .  \nThe paper is designed and divided into seven sections. The first part is the introduction that deals with motivation and the paper aims to highlight the importance of machine learning tools and techniques to predict heart disease risks. The second section delineates the literature review done on the existing research works that are aligned with our chosen topic and the succeeding third chapter draws upon the inferences derived from such review. The fourth part explicates the methodology used and the fifth section highlights the findings and discussion, and finally, the sixth part deals with the conclusion followed by references, as a part of the seventh section.  \n\n| IJRAR24D1253 | International Journal of Research and Anal","cbCainZfqGw2lQKL","https://ap.wps.com/l/cbCainZfqGw2lQKL","pdf",1166497,1,9,"English","en",105,"# Introduction\n## Research objective and motivation\n## Paper structure\n# Literature Review\n## K-Means clustering and challenges\n## Random Forest regression and accuracy results\n## SVM, KNN, and Logistic Regression comparisons","[{\"question\":\"What is the main goal of the proposed study?\",\"answer\":\"To comprehend, assess, and analyze how machine learning models can predict heart disease risk using clinical data for early detection and risk management.\"},{\"question\":\"Which machine learning classifiers are included in the paper?\",\"answer\":\"The paper evaluates Logistic Regression, Random Forest, Decision Tree, Naive Bayes, k-nearest Neighbors, Neural Networks, and Support Vector Machine (SVM).\"},{\"question\":\"Which model achieves the highest accuracy and what does it imply?\",\"answer\":\"Support Vector Machine (SVM) shows the highest accuracy at 91.51%, indicating strong predictive ability for cardiovascular risk assessment in clinical settings.\"}]","Advancements In Heart Disease Prediction - A Machine Learning Approach For Early Detection And Risk Assessment - Abstract And Model Evaluation | PDF",1785817130,23,{"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},"advancements-in-heart-disease-prediction-a-machine-learning-approach-for-early-detection-and-risk-assessment-abstract-and-model-evaluation","",{"@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/advancements-in-heart-disease-prediction-a-machine-learning-approach-for-early-detection-and-risk-assessment-abstract-and-model-evaluation/123527/",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","2026-08-04",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},"What is the main goal of the proposed study?","Question",{"text":76,"@type":77},"To comprehend, assess, and analyze how machine learning models can predict heart disease risk using clinical data for early detection and risk management.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning classifiers are included in the paper?",{"text":81,"@type":77},"The paper evaluates Logistic Regression, Random Forest, Decision Tree, Naive Bayes, k-nearest Neighbors, Neural Networks, and Support Vector Machine (SVM).",{"name":83,"@type":74,"acceptedAnswer":84},"Which model achieves the highest accuracy and what does it imply?",{"text":85,"@type":77},"Support Vector Machine (SVM) shows the highest accuracy at 91.51%, indicating strong predictive ability for cardiovascular risk assessment in clinical settings.","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},[94,98,102,106,111,116,119,124,128,131,135],{"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":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"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":107,"slug":138},19,"General","general"]