[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124825-en":3,"doc-seo-124825-105":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},124825,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Cardiovascular Disease Prediction using Machine Learning Ensemble Methods - Abstract and Model Evaluation","Early cardiovascular disease diagnosis lowers risk and improves health outcomes, addressing the high mortality historically associated with heart-related conditions. The study applies data mining with minimal human intervention by building an ensemble-based prediction model. Boosting and bagging approaches are implemented using Random Forest, Adaptive Boosting, Gradient Boosting, and XGBoost, followed by performance comparison. A mobile application is developed to collect clinical attributes and display the predicted probability of heart disease, with accuracy evaluation.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-6 Year 2023 Page 616:621  \nCardiovascular Disease Prediction using Machine Learning Ensemble  \nMethods  \nVandana Joshi1*, Shruthi R2, Varshitha B3, Devarasetty Vivek Kumar4, Mallikarjuna  \nM5  \n1,2,3,4,5School of Computer Science and Engineering, REVA University  \n[1](1r17cs526@cit.reva.edu.in)[r17cs526@cit.reva.edu.in](1r17cs526@cit.reva.edu.in)  \n[2](2r17cs444@cit.reva.edu.in)[r17cs444@cit.reva.edu.in](2r17cs444@cit.reva.edu.in)  \n[3](3r17cs456@cit.reva.edu.in)[r17cs456@cit.reva.edu.in](3r17cs456@cit.reva.edu.in)  \n[4](4r17cs433@cit.reva.edu.in)[r17cs433@cit.reva.edu.in](4r17cs433@cit.reva.edu.in)  \n[5](5mallikarjuna.m@reva.edu.in)[mallikarjuna.m@reva.edu.in](5mallikarjuna.m@reva.edu.in)  \n*Corresponding author’s E-mail: [r17cs526@cit.reva.edu.in](r17cs526@cit.reva.edu.in)  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 29 Nov 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Recently the main cause of death occurring due to cardiovascular disease has happened even in the past. Early diagnosis of the disease can be assessed to reduce the high risk and ensure healthiness. Data mining techniques have been significantly used as it helps in zero or less intervention of humans and it is seen as the best technique as it gives precise result with the best accuracy. The study is conducted on ensemble methods and built a model using boosting and bagging classifiers. The objective of this work is to design and implement a heart disease prediction system using machine learning ensemble methods namely, Random Forest, Adaptive Boosting, Gradient Boosting, and XGBoost. The effective performance of the applied ensemble techniques is analyzed, anda mobile application is developed for the same. The proposed mobile application is built as a user interface that accepts data based on clinical attributes concerning heart disease. This mainly helps in the medical field such as laboratories that incorporate the developed model. The outcome of the proposed model predicts the probability of a person suffering from heart disease. The accuracy of the models is evaluated.\u003Cbr>Keywords: Machine Learning, Ensemble Learning, Adaptive Boosting, Gradient Boosting, XGBoost, Mobile Application |\n| --- | --- |\n\n1. Introduction  \nCoronary heart disease is the most common type of heart disease. Millions of deaths occur among adults and old people. Cardiovascular disease prediction is being predicted in the proposed model. It is seen that various Machine Learning techniques would give accurate predictions compared to any other techniques. The ensemble technique has been used in the given model to enhance the accuracy to the best. A handheld device interface has been developed which takes the user input based on the clinical parameters such as name, age, sex, cholesterol, bp, etc. which are processed further. A literature survey is made on cardiovascular disease prediction by going through several technical papers and it is observed that the accuracy of the machine learning ensemble model has a higher standard compared to other models. In section III, a brief description of the proposed model is made by describing the flow of procedure involved and the algorithms used in the proposed model is given with an explanation. Section IV presents the result after a comparative study is performed on several ensemble classifiers such as Random Forest, Adaptive boosting, Gradient Boosting and, XGBoost. The one with the highest accuracy has been incorporated in the mobile application and the output is displayed on the same.  \nLiterature Survey  \nO. Terrada [et.al](et.al)., mainly focused on predicting if a patient is suffering from atherosclerosis which isone type of cardiovascular disease by using two supervised ML algorithms. ANN and Adaptive Boosting techniques are used, and databases are collected from UCI and Z-Alizadeh datasets. The comparative study has been made amon","cbCain8dGskDdLO6","https://ap.wps.com/l/cbCain8dGskDdLO6","pdf",375892,1,6,"English","en",105,"# Introduction\n# Literature Survey\n## Comparative studies with machine learning classifiers\n# Proposed Ensemble Model and Procedure Flow\n# Result Analysis and Comparative Evaluation","[{\"question\":\"What ensemble classifiers are used for cardiovascular disease prediction?\",\"answer\":\"The study builds an ensemble model using Random Forest, Adaptive Boosting, Gradient Boosting, and XGBoost classifiers.\"},{\"question\":\"How does the proposed system collect and process inputs?\",\"answer\":\"A mobile application collects clinical attributes such as age, sex, cholesterol, and blood pressure, then processes them for prediction.\"},{\"question\":\"What is the main output of the prediction model?\",\"answer\":\"The model predicts the probability that a person is suffering from heart disease, and accuracy is evaluated through comparative performance.\"}]","Cardiovascular Disease Prediction using Machine Learning Ensemble Methods - 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