[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124678-en":3,"doc-seo-124678-105":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},124678,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Optimized Classification of Cardiovascular Disease Using Machine Learning Paradigms","Cardiovascular and chronic respiratory diseases cause nearly 19 million deaths annually, making early identification of critical symptoms essential to reduce mortality. This study develops a machine learning (ML) framework to forecast cardiovascular disease (CVD) risk and key symptoms using individual demographic attributes (age, gender, ethnicity, body mass) and lifestyle factors. Multiple models are trained and tested to compare performance using training/testing results and evaluation metrics including precision, recall, and F1-score. Naïve Bayes and XGBoost achieve the highest accuracies of 92.31% and 92.34%.","VFAST Transactions on Software Engineering [http://vfast.org/journals/index.php/VTSE@ 2023](http://vfast.org/journals/index.php/VTSE@ 2023), ISSN(e): 2309-3978, ISSN(p): 2411-6246  \nVolume 11, Number 2, April-June 2023 pp: 140-148  \nOptimized Classification of Cardiovascular Disease Using Machine Learning Paradigms  \nFouzia Kanwal 1, Mr. Kamran Abid 1, Muhammad Sajid Maqbool2, Naeem Aslam 1, Muhammad Fuzail1  \n1Department of computer science, NFC Institute of Engineering and Technology Multan, Pakistan 2Department of computer science, Bahauddin Zakariya University Multan, Pakistan  \n*Corresponding author email: [fouzia53391@gmail.com](fouzia53391@gmail.com)  \nABSTRACT  \nNearly 19 million people die each year from cardiovascular and chronic respiratory diseases, which are a global threat. It is necessary to address the causes of these diseases because of the high death rate. The investigation uncovered a number of causes, but the inability to forecast these diseases symptoms is by far the most significant. In this work, we developed a method for anticipating these diseases crucial symptoms, which will aid in early disease diagnosis and allow patients to begin treatment. This research will introduce a new computational medicine research using machine learning (ML) paradigms to forecast cardiovascular disease (CVD). Data were processed by methods in sequence with various parameters. different models created that predicts CVD risk based on individual age, gender, ethnicity, body mass etc., and lifestyle factors. The research will also focus on performing complete comparison of ML models. We will apply Five ML based algorithems such as Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBOOST and Random Forest and evaluate these models on the basis of Training and Testing and also calculated the Presicion Recall and F1-Score for each model. Naïve Bayes and XGBOOST Classier perform better with accuracy of 92.31 and 92.34 percent as compared to other models.  \nKeywords:  \nCardio Vascular, Cardio Vascular Disease, Data Science, Student Performance Prediction, Machine Learning, Deep Learning  \nJOURNAL INFO  \nHISTORY: Received: May 24, 2023 Accepted: June 26, 2023 Published: June 30, 2023  \n1 INTRODUCTION  \nThe World Health Organization (WHO) found that heart attacks and strokes are responsible for 17.5 million deaths worldwide. Furthermore, low- and middle-income nations account for the majority of the deaths from cardiovascular diseases, which account for almost 75% of all deaths worldwide. The third most prevalent illness worldwide and a major cause of death is heart disease, sometimes referred to as cardiovascular disease. CVDs encompass conditions that affect the heart, such as coronary heart disease, cerebrovascular illness, rheumatic heart disease, and others. bad eating habits, inactivity, smoking, and drinking too much alcohol often cause them. In this blog post, we will be using machine learning and deep learning paradigms to forecast cardiovascular disease rates on a global scale [1] .“Heart failure is a condition where the heart loses its ability to pump enough oxygen-rich blood to meet the body's needs”. This leads to a retention of fluid, breathing problems, and swelling in the lower legs (peripheral edema) . Central edema happens when the brain gets too much fluid and can result in mental confusion or drowsiness. Heart failure affects men and women equally and can happen at any age. Symptoms vary from person to person; however, they can include shortness of breath, chest pain, unusual weight gain or loss, fatigue, weakness or numbness in one arm or leg; dizziness or fainting episodes; and needing to urinate more often than normal. Heart diseases are the most significant  \nissue faced by people. It is caused by high cholesterol, high blood pressure, and other chronic diseases. Therefore, a large number of Americans have reduced their risk factors for heart disease by eating healthier foods and maintaining ph","cbCailnJpSB4bAoT","https://ap.wps.com/l/cbCailnJpSB4bAoT","pdf",761796,1,9,"English","en",105,"# Abstract\n# Introduction\n## Global burden and causes of cardiovascular disease\n## Motivation for early diagnosis using ML/DL\n# Keywords","[{\"question\":\"What problem does the study address about cardiovascular disease?\",\"answer\":\"The research targets the difficulty of forecasting cardiovascular disease symptoms and risk, aiming to support earlier diagnosis and treatment initiation.\"},{\"question\":\"Which machine learning algorithms are evaluated in the work?\",\"answer\":\"The study compares five ML algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBoost, and Random Forest.\"},{\"question\":\"How are the models evaluated and which models perform best?\",\"answer\":\"Models are assessed using training/testing results and metrics including precision, recall, and F1-score. Naïve Bayes and XGBoost perform best with accuracies of 92.31% and 92.34% respectively.\"}]","Optimized Classification of Cardiovascular Disease Using Machine Learning Paradigms | PDF",1785893862,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimized-classification-of-cardiovascular-disease-using-machine-learning-paradigms","",{"@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/optimized-classification-of-cardiovascular-disease-using-machine-learning-paradigms/124678/",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","2026-08-05",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 problem does the study address about cardiovascular disease?","Question",{"text":75,"@type":76},"The research targets the difficulty of forecasting cardiovascular disease symptoms and risk, aiming to support earlier diagnosis and treatment initiation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are evaluated in the work?",{"text":80,"@type":76},"The study compares five ML algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGBoost, and Random Forest.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and which models perform best?",{"text":84,"@type":76},"Models are assessed using training/testing results and metrics including precision, recall, and F1-score. 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