[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119831-en":3,"doc-seo-119831-105":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},119831,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","A Comprehensive Analysis on Risk Prediction of Heart Disease using Machine Learning Models - focus on DT, K-NN, RF and SVM","Heart disease causes a major share of global mortality and morbidity, making early detection and ongoing monitoring essential to reduce deaths and severity. This work addresses heart-disease risk prediction as a clinically challenging task in large and sometimes incomplete datasets by applying machine learning classification models. A comparative approach trains Decision Tree, K-Nearest Neighbors, Random Forest, and Support Vector Machine, evaluating performance via accuracy, precision, recall, and specificity, with SVM showing the strongest overall results across cases.","A Comprehensive Analysis on Risk Prediction of Heart Disease using Machine Learning Models  \nDr. Pokkuluri Kiran Sree1 , Dr.M. Prasad2, Dr. Raja Rao PBV3, Mr Chintha Venkata Ramana4, Mr. P TSatyanarayana Murty5, Mr. A. Satya Mallesh6, Mr. P J R Shalem Raju7  \n1-7, Department of Computer Science And Engineering , Shri Vishnu Engineering College for Women  \nBhimavaram,India, 534202  \ne-mail: [drkiransree@gmail.com](drkiransree@gmail.com)  \nAbstract—Most of the deaths worldwide are caused by heart disease and the disease has become a major cause of morbidity for many people. In order to prevent such deaths, the mortality rate can be greatly reduced through regular monitoring and early detection of heart disease. Heart disease diagnosis has grown to be a challenging task in the field of clinically provided data analysis. Predicting heart disease is a highly demanding and challenging task with pure accuracy, but it is easy to figure out using advanced Machine Learning (ML) techniques. A Machine Learning approach has been shown to predict heart disease in this approach. By doing this, the disease can be predicted early and the mortality rate and severity can be reduced. The application of machine learning techniques is advancing significantly in the medical field. Interpreting these analyzes in this methodology, which has been shown to specifically aim to discover important features of heart disease by providing ML algorithms for predicting heart disease, has resulted in improved predictive accuracy. The model is trained using classification algorithms such as Decision Tree (DT), K-Nearest Neighbors (K-NN), Random Forest (RF), Support Vector Machine (SVM) . The performance of these four algorithms is quantified in different aspects such as accuracy, precision, recall and specificity. SVM has been shown to provide the best performance in this approach for different algorithms although the accuracy varies in different cases.  \nKeywords-Heart Disease Prediction, Machine Learning (ML), Random Forest (RF), Decision Tree, Support Vector Machine (SVM) K-Nearest Neighbors (K-NN) .  \nI. INTRODUCTION  \nOne of the body's most significant organs, after the brain, is the heart. Blood circulation throughout the body is the heart's primary function. Heart disease is any condition that has the potential to disrupt the heart's function. There are numerous types of heart diseasesworldwide.  \nThe World Health Organization (WHO) reports that heart attacks and strokes are the leading causes of death worldwide. According to WHO, heart diseases cause many deaths worldwide each year. Cardiovascular disorders were the cause of more than 50% of deaths in the US and other countries. In many regions, it is one of the leading causes of death. It is regarded as the main cause of death in adults.  \nHeart disease is one of the leading causes of death worldwide in developed nations. Heart failure risk can be increased by a number of circumstances. Medical experts have classified risk factors into two categories: risk factors that cannot be adjusted and risk factors that can be changed. Unchanged risk factors include family history, gender, and age. Modifiable risk factors include obesity, smoking, physical diseases, high blood pressure, and excessive happinessCardiovascular problems early  \nidentification can lower the mortality rates, due to lack of  \ninformation, many are not aware of the earlier factors that lead tocardiovascular diseases.  \nHealthcare organisations are attempting to diagnose the disease in its early stages. The disease is typically only discovered in the last stages or after death. This occurrence has led healthcare organizations to aim to identify the diseases at an early stages.  \nHeart diseases can be categorised as either coronary heart disease or cardiovascular disease. The term\"cardiovascular disease\" refers to a number of conditions that have an impact on the heart, blood vessels, and the body's circulatory and pumping systems. Various d","cbCaimF7L0uq3ODY","https://ap.wps.com/l/cbCaimF7L0uq3ODY","pdf",306215,1,6,"English","en",105,"# Introduction\n## Heart disease overview and risk factors\n## Motivation for early identification\n# Machine Learning Approach\n## Model training and algorithms\n## Evaluation metrics","[{\"question\":\"Why is early prediction of heart disease important?\",\"answer\":\"Heart disease leads to high mortality, and early identification helps lower mortality rates by enabling timely intervention before late-stage discovery.\"},{\"question\":\"Which machine learning models are used for heart disease risk prediction?\",\"answer\":\"The approach trains Decision Tree (DT), K-Nearest Neighbors (K-NN), Random Forest (RF), and Support Vector Machine (SVM) classifiers.\"},{\"question\":\"How is the performance of the models evaluated?\",\"answer\":\"Model performance is quantified using accuracy, precision, recall, and specificity to compare results across algorithms.\"}]","A Comprehensive Analysis on Risk Prediction of Heart Disease using Machine Learning Models - 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