[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123704-en":3,"doc-seo-123704-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123704,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Statistical Machine Learning Algorithm for Predicting the Risk Factors in Heart Disease","Heart disease is a leading non-communicable cause of mortality worldwide, with WHO estimates indicating substantial and rising death figures through 2020, 2030, and beyond. This prospective study evaluates modified and non-modified risk factors including age, gender, family history, hypertension, diabetes, obesity, blood pressure, smoking, alcohol intake, exercise, and heart rate. Data from 336 patients at JSS hospital support risk prediction using machine learning, with Naïve Bayes achieving reported 94% accuracy. Results indicate higher occurrence in males and elderly individuals, with obesity emphasized as a key contributor followed by hypertension, alcohol intake, smoking, exercise, and age.","Manuscript 1045  \nStatistical Machine Learning Algorithm for Predicting the Risk Factors in Heart Disease  \nChaithra N  \nShalini H. Doreswamy Pallavi N  \nFollow this and additional works at: [https://rescon.jssuni.edu.in/ijhas](https://rescon.jssuni.edu.in/ijhas)  \nORIGINAL STUDY  \nStatistical Machine Learning Algorithm for Predicting the Risk Factors in Heart Disease  \nChaithra Nagaraju a, *, Pallavi Nagaraju b, Shalini Doreswamy c  \na Division of Medical Statistics, School of Life Sciences, JSS Academy of Higher Education & Research (JSS AHER), Mysuru 570015, Karnataka, India  \nb School of Life Sciences and Natural Sciences, JSS Academy of Higher Education & Research (JSS AHER), Mysuru 570015, Karnataka, India  \nc Center of Excellence in Molecular Biology and Regenerative Medicine (CEMR), Department of Biochemistry, JSS Medical College, JSS Academy of Higher Education & Research (JSS AHER), Mysuru 570015, Karnataka, India  \nAbstract  \nHeart disease is one of the major non-communicable disease and leading cause of mortality in the world. According to WHO heart disease is taking about nearly 17.9 million lives of people each year. Its mortality forecasts indicate a rise in global annual deaths to 20.5 million in 2020 and as high as 24.2 million by 2030. Risk factors are one of the most powerful predictors of heart disease. The study includes modified and non-modified risk factors that contribute to the disease such as Age, Gender, Family history, Hypertension, Diabetics, Obesity, Blood Pressure, Smoking, Alcohol intake, Exercise and Heart rate. Machine learning is one of the most useful techniques that can help researchers, entrepreneurs, and individuals for extracting valuable information from sets of data. The objective of this study is to highlight the utility and application of machine learning techniques for the prediction of heart disease to facilitate experts in the healthcare domain. A total of 336 patients were examined and their personal and medical data were collected in JSS hospital. This prospective study was consisting of 55% patients are free from the heart disease and 45% have heart disease. From the result, it has been determined that males are more likely to develop the heart diseases than females and very common in elderly persons. The accuracy of the Naïve Bayes model is found to be 94%, Obesity plays a vital role in getting the disease followed by hypertension, alcohol intake, smoking, exercise and age has more impact on developing the heart disease.  \nKeywords: Heart disease, Risk factors, Odd ratio, Naïve Bayes algorithm  \n1. Introduction  \nH eart disease is the main cause of death in the  \nworld [1]. According to WHO heart disease is taking about nearly 17.9 million lives of people each year. It has also reported that 4 out of 5 heart disease deaths are due to strokes and heart attack while 1/ 3rd of these deaths occurs in the people who are below 70 years of age [2]. Its mortality forecasts indicate a rise in global annual deaths to 20.5 million in 2020 and as high as 24.2 million by 2030. They constitute 31.5% and 32.5% of all global deaths respectively, for males it has been predicted that the  \npercentage of male dying due to CHD rises from 13.1% in 2010 to 14.9% in 2030 while it drops for the female from 13.6% in 2010 to 13.1% in 2030. Stroke deaths rise from 9.2% to 10.4% for males and 11.5%e11.8% for females [3].  \nThe ‘heart disease’ refers to disease of the heart and the fluctuating functions of the blood vessels init [4]. Heart disease is often used interchangeably with the term cardiovascular disease (CVD) . A major change has been made in the emphasis from communicable diseases to a new epidemic of NonCommunicable Diseases (NCDs) and their side effects. Cardiovascular Disease (CVD), Cancer, and  \nReceived 16 February 2023; accepted 15 July 2023.  \nAvailable online 28 September 2023  \n* Corresponding author at: Division of Medical Statistics, School of Life Sciences, JSS Academy of Higher Educat","cbCaijO7Kv1jM3xG","https://ap.wps.com/l/cbCaijO7Kv1jM3xG","pdf",382052,1,"English","en",105,"# Introduction\n## Heart disease burden and definitions\n## Risk factors and prevention strategy\n# Study objective and data collection\n## Prospective cohort and patient distribution\n# Methods and modeling approach\n## Naïve Bayes machine learning\n# Results and key findings\n## Accuracy and most influential risk factors","[{\"question\":\"What risk factors were included to predict heart disease?\",\"answer\":\"The study considers both modified and non-modified risk factors such as age, gender, family history, hypertension, diabetes, obesity, blood pressure, smoking, alcohol intake, exercise, and heart rate.\"},{\"question\":\"What machine learning model was used and what accuracy was reported?\",\"answer\":\"Naïve Bayes was applied, and the reported accuracy is 94%.\"},{\"question\":\"Which factors were found to have the strongest influence on heart disease development?\",\"answer\":\"Obesity is reported as a vital contributor, followed by hypertension and alcohol intake, then smoking, exercise, and age with comparatively higher impact.\"}]","Statistical Machine Learning Algorithm for Predicting the Risk Factors in Heart Disease | PDF",1785818098,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"statistical-machine-learning-algorithm-for-predicting-the-risk-factors-in-heart-disease","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/statistical-machine-learning-algorithm-for-predicting-the-risk-factors-in-heart-disease/123704/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What risk factors were included to predict heart disease?","Question",{"text":74,"@type":75},"The study considers both modified and non-modified risk factors such as age, gender, family history, hypertension, diabetes, obesity, blood pressure, smoking, alcohol intake, exercise, and heart rate.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What machine learning model was used and what accuracy was reported?",{"text":79,"@type":75},"Naïve Bayes was applied, and the reported accuracy is 94%.",{"name":81,"@type":72,"acceptedAnswer":82},"Which factors were found to have the strongest influence on heart disease development?",{"text":83,"@type":75},"Obesity is reported as a vital contributor, followed by hypertension and alcohol intake, then smoking, exercise, and age with comparatively higher impact.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]