[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121003-en":3,"doc-seo-121003-105":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":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},121003,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Evaluation of Cardiovascular Disease in Diabetic Patients Using Machine Learning Techniques","Cardiovascular disease prediction for diabetic patients using machine learning focuses on improving early risk identification and clinical decision support. The work compares multiple models, including decision trees, AdaBoost, support vector machines, artificial neural networks, and a customized ANN, using medical test results and general information datasets. Particle swarm optimization and k-nearest neighbors are applied for feature selection and dimensionality reduction. Comparative evaluation across multiple assessment criteria indicates the proposed model achieves the highest accuracy for suitability.","Evaluation of cardiovascular disease in diabetic patients using  \nmachine learning techniques  \nSilpa Nrusimhadri1, Sangram Keshari Swain1, Veeranki Venkata Rama Maheswara Rao2, Shiva Shankar Reddy3, Mahesh Gadiraju3  \n1Department of Computer Science and Engineering, Centurion University of Technology and Management, Bhubaneswar, India 2Shri Vishnu Engineering College for Women (A), Bhimavaram, India  \n3Sagi RamaKrishnam Raju Engineering College (A), Bhimavaram, India  \n\n| Article history:\u003Cbr>Received Nov 14, 2023 Revised Jan 14, 2024 Accepted Jan 25, 2024 | Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN) . Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Cardiovascular disease Coronary artery disease Deep learning Machine learning\u003Cbr>Particle swarm optimization |  |\n\nCorresponding Author:  \nSilpa Nrusimhadri  \nDepartment of Computer Science and Engineering, Centurion University of Technology and Management Bhubaneswar, Odisha, India  \n[Email: nrusimhadri.silpa@gmail.com](Email: nrusimhadri.silpa@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nWorldwide, heart attacks are the leading cause of death. Causes include stress, genetics, hypertension, and other issues. \"Heart disease\" is often an umbrella term for various conditions that interfere with the heart's standard structure and function. The 75% or more of the victims were from low-and middleincome countries. Coronary heart disease (CHD), i.e., heart attack, is the most prevalent and lethal of all heart conditions [1] . An estimated 805,000 Americans have a heart attack yearly in the United States, with one occurrence reported every 40 seconds. It can be challenging to identify individuals who are at high risk of health issues due to the range of risk factors involved, including diabetes, hypertension, hyperlipidemia, and others. Doctors and scientists have started using machine learning (ML) techniques to create screening tools as they are better at detecting patterns and classifying data than traditional statistical methods [2]–[4] . Cardiovascular diseases (CVD) include all heart diseases. However, not all heart disorders are cardiovascular ailments. Heart or blood vessel diseases are referred to as CVD.  \nThese methods can estimate outcome probability for individuals aged 30 to 74 throughout 4 to 12 years. Compare the equations with a new CHD prediction equation to see whether a single profile accurately predicts associated endpoints. Individual shapes for diastolic (DBP) and systolic blood pressure (SBP) arepresented for all outcomes. An approach to calculating confidence intervals for expected probabilities, hazard  \nratios, and excess risk estimations is described. Blood is pumped around the body through the circulatory system, also called the cardiovascular system. The circulatory system's primary components are the heart, veins, arteries, and blood capillaries. Heart disorders result from their inability to carry out their ","cbCaioJ4L1kpMloK","https://ap.wps.com/l/cbCaioJ4L1kpMloK","pdf",841196,1,13,"English","en",105,"# Abstract\n## Introduction\n## Machine Learning Methods and Models","[{\"question\":\"What problem does this research address?\",\"answer\":\"It addresses early evaluation and prediction of cardiovascular disease risk in diabetic patients to support more accurate clinical decision-making.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The study compares decision trees, AdaBoost, support vector machines, artificial neural networks, and a customized ANN.\"},{\"question\":\"How does the proposed approach improve model performance?\",\"answer\":\"It uses particle swarm optimization and k-nearest neighbors for feature selection and applies evolutionary and neural methods to reduce dimensionality, then evaluates models using multiple criteria with the best accuracy reported.\"}]","Evaluation of Cardiovascular Disease in Diabetic Patients Using Machine Learning Techniques | PDF",1785733271,33,{"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},"evaluation-of-cardiovascular-disease-in-diabetic-patients-using-machine-learning-techniques","",{"@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/evaluation-of-cardiovascular-disease-in-diabetic-patients-using-machine-learning-techniques/121003/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this research address?","Question",{"text":75,"@type":76},"It addresses early evaluation and prediction of cardiovascular disease risk in diabetic patients to support more accurate clinical decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"The study compares decision trees, AdaBoost, support vector machines, artificial neural networks, and a customized ANN.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach improve model performance?",{"text":84,"@type":76},"It uses particle swarm optimization and k-nearest neighbors for feature selection and applies evolutionary and neural methods to reduce dimensionality, then evaluates models using multiple criteria with the best accuracy reported.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"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":106,"slug":138},19,"General","general"]