[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123706-en":3,"doc-seo-123706-105":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},123706,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Prediction of Cardiovascular disease using machine learning algorithms on healthcare data - Journal Article","Cardiovascular Disease (CVD) remains a leading global cause of death, and earlier assessment can reduce mortality from events such as heart attacks and strokes. This study applies multiple machine learning techniques to a healthcare dataset to estimate a 10-year risk of future coronary heart disease (CHD). Data are drawn from the Framingham and Massachusetts cardiovascular study, and model performance is compared using accuracy to guide algorithm selection for individual patients.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 Issue Special Issue-5 Year 2023 Page 1215:1226  \nPrediction of Cardiovascular disease using machine learning  \nalgorithms on healthcare data Salmoli Chandra*, J. Chanda **, Sukumar Chandra***  \n*JIOInstitute, Sector-4, ULWE, NAVI MUMBAI, MAHARASHTRA-410206, India  \n** B.P. P. I. T & M., Maulana Abul Kalam Azad University of Technology, Kolkata, India  \n*** Pingla Thana Mahavidyalaya, Maligram, PaschimMedinipur, West Bengal, India  \n*** Corresponding Author :  \nMail address: [sukumarchandra14@gmail.com](sukumarchandra14@gmail.com)  \n\n| Article History\u003Cbr>Received: 08July2023\u003Cbr>Revised: 29 Aug 2023\u003Cbr>Accepted: 02 Oct 2023\u003Cbr>CCLicense\u003Cbr>CC-BY-NC-SA 4.0 | ABSTRACT:\u003Cbr>Cardiovascular Disease (CVD) is a leading cause of death worldwide, with the potential to cause serious conditions such as heart attacks and strokes. Early assessment of CVD can significantly reduce mortality rates. In recent studies, machine learning algorithms have been applied to Electronic Health Records (EHR) to estimate risk factors for myocardial infarction. This article explores the use of various machine learning techniques on a healthcare dataset to predict a 10-year risk of future coronary heart disease (CHD). The dataset used in this study was obtained from the Framingham and Massachusetts cardiovascular study. We found that our models achieved varying levels of accuracy: 64% for logistic regression, 83% for Naïve Bayes classifier, 42% for Support Vector Machine (SVM), 65% for Random Forest, 78% for KNN classifier, and 70% for XGBOOST classifier. It is revealed that a patient with no history of heart disease may benefit from an algorithm such as Naive Bayes Classifier, while an older patient with a history of heart disease may require an algorithm such as Support Vector Machine. These factors can help guide the physician in selecting the most appropriate algorithm for each individual patient, ensuring that the diagnosis is as accurate as possible and that the treatment plan is tailored to meet the patient's unique needs.\u003Cbr>Keywords: Support Vector Machine, Cardiovascular diseases, Machine learning |\n| --- | --- |\n\nIntroduction:  \nCardiovascular Disease (CVD) is a major health concern worldwide, causing millions of deaths each year with increasing rates [1, 2, 3] . Cardiologists  \nand surgeons often struggle with estimating the risk of heart failure. It is crucial to accurately predict the risk of heart failure in order to identify and treat complex cardiovascular diseases at an early stage. Machine learning models from  \n1215  \nAvailable online at: [https://jazindia.com](https://jazindia.com)  \nPrediction of Cardiovascular disease using machine learning algorithms on healthcare data  \nmedical databases can be used for that purpose. Ishaq et al. [4] has made an attempt to predict heart failure disease using SMOTE and data mining techniques.  \nMachine learning techniques can be more useful than traditional modeling techniques in some cases. Turkmenoglu and Yildiz [5], as well as Chicco and Jurmen [6], have used machine learning models in their data analysis. Different machine learning models can lead to different conclusions, and it is important to choose the appropriate model for a specific use case. Therefore, it is necessary to carefully evaluate the performance of each model to determine which one is best suited for the task at hand.  \nTo further explore the use of machine learning in predicting cardiovascular  \ndisease, this study utilizes the Framingham Heart Study data set. By applying multiple machine learning techniques and using the Python framework, we aim to gain valuable insights into CVD prediction. The Framingham data set contains 3,390 records and 17 attributes related to patient information, making it an extremely useful resource for predicting CVD.  \nMETHODOLOGY  \nData collection was made by many researchers from the Framingham dataset [7, 8, 9 ] . Here we have also used the Fr","cbCaiv0wVIlSfVvq","https://ap.wps.com/l/cbCaiv0wVIlSfVvq","pdf",702124,1,12,"English","en",105,"# Introduction\n## Methodology","[{\"question\":\"What problem does the study address in cardiovascular care?\",\"answer\":\"The study targets early assessment of cardiovascular risk by predicting a patient’s 10-year risk of future coronary heart disease (CHD), which can support timely diagnosis and treatment planning.\"},{\"question\":\"Which dataset is used for the experiments?\",\"answer\":\"The experiments use the Framingham and Massachusetts cardiovascular study dataset (Framingham Heart Study data) with patient records and multiple health-related attributes.\"},{\"question\":\"Which machine learning models are evaluated and how do their accuracies compare?\",\"answer\":\"The study evaluates logistic regression, Naïve Bayes, SVM, random forest, KNN, and XGBOOST, reporting accuracies of 64%, 83%, 42%, 65%, 78%, and 70% respectively.\"}]","Prediction of Cardiovascular disease using machine learning algorithms on healthcare data - 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