[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127130-en":3,"doc-seo-127130-105":30,"detail-sidebar-cat-0-en-105":95},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},127130,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Research and Application of Heart Disease Prediction Model Based on Machine Learning","Heart disease ranks as a leading cause of death worldwide, making early and accurate prediction essential for assisting physicians in initial judgments and improving survival outcomes. This study develops a machine learning–based diagnostic support approach using a heart disease dataset, training multiple models on key health features and validating performance on a test set. Logistic regression and random forests show strong results and practical value. Future work can stack models and refine data sources to enhance real-world performance and efficiency.","Research and Application of Heart Disease Prediction Model Based on Machine Learning  \nYongli Bao  \nInternational College, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China  \nAbstract. As heart disease has become the leading cause of death worldwide, early and accurate prediction is crucial to help doctors make initial judgments about patients and improve their survival rates. This study aims to improve the accuracy and efficiency of heart disease prediction through Machine learning (ML) methods to help medical diagnosis. A heart disease dataset was used in the study, and multiple ML models were used to analyze multiple key health features, and the model performance was verified through a test set. This paper concludes that Logistic regression and random forests perform well in this task and have high practical value. Future research can stack models and optimize data sources to improve the practical performance of the model. This study provides a basic framework for building an intelligent medical auxiliary diagnosis system, which helps to achieve early prevention and timely judgment of heart disease, thereby improving the overall efficiency of medical services.  \n1 Introduction  \nWorldwide, cardiovascular diseases (CVDs) are the cause of a relatively high percentage of people's deaths. According to the definition of the WHO, CVDs are a class of disorders affecting the heart and blood vessels. Their fundamental characteristic is the blockage of blood arteries, which prevents blood from reaching the heart and brain adequately and impairs the heart's and the brain's tissues' capacity to function normally [1]. According to the relevant epidemiologic and preventive statistical reports, it is evident that the lethality of CVDs is increasing globally. Compared with 1990, the mortality rate of CVDs such as ischemic heart disease has increased significantly [2] . This shows that cardiovascular disease is still one of the major diseases that endanger human health. There are many causes of cardiovascular disease, including diet, social environment, psychological factors, etc. These factors also increase the difficulty of diagnosing cardiovascular disease. Therefore, diagnosing cardiovascular disease is a complex process but has important significance.  \nHowever, diagnosing cardiovascular disease requires a lot of medical resources. Medical experts need to spend a lot of time reading the patient's medical history to help them make more accurate judgments. ML is one of the branches of artificial intelligence. The emergence of ML makes up for the limitations of traditional medical methods. ML can help doctors  \nCorresponding author: [2021215000@stu.cqupt.edu.cn](2021215000@stu.cqupt.edu.cn)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nsolve the problem of spending a lot of time reading patient records and making preliminary judgments. ML obtains clinical decisions made by a large number of doctors and medical records of millions of patients through a large amount of training. Currently, a variety of AI models have begun to be gradually put into use in the medical industry, such as prediction models for cancer in patients [3,4] . Different ML models have also been used in the health service industry [5] . Among them, ML has great advantages in predicting and diagnosing CVDs. ML can quickly process a large number of past cases to assist decision-makers in making diagnoses [6] . For example, Saba Bashir et al. used naive Bayes, decision tree, SVMand other methods to predict heart disease with an accuracy of 87. 37%[7] . Dutta used a convolutional neural network (CNN) to diagnose coronary heart disease [8] . V Krishnaiah et al. used the fuzzy K-NN method to establish a heart disease prediction model [9] .  \nThe pu","cbCaibqMTmE0M7Ew","https://ap.wps.com/l/cbCaibqMTmE0M7Ew","pdf",325735,2,1,"English","en",105,"# Introduction\n# Material and Method\n## Data source\n## Data preprocessing\n## Feature selection and dimensionality reduction","[{\"question\":\"What is the main goal of this research?\",\"answer\":\"To improve the accuracy and efficiency of heart disease prediction using machine learning methods for medical diagnosis support.\"},{\"question\":\"Which dataset and features are used in the study?\",\"answer\":\"The study uses the heart disease dataset from Cleveland UCI, with 14 attributes, and analyzes key health features derived from those attributes.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Multiple machine learning models are validated using a test set and evaluated with metrics such as accuracy, precision, recall, and F1 score.\"},{\"question\":\"What results do the models achieve, and what is recommended for future work?\",\"answer\":\"Logistic regression and random forests perform well with high practical value; future work can stack models and optimize data sources to improve real-world performance.\"}]","Research and Application of Heart Disease Prediction Model Based on Machine Learning | PDF",1785937049,20,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"research-and-application-of-heart-disease-prediction-model-based-on-machine-learning","",{"@graph":36,"@context":89},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/research-and-application-of-heart-disease-prediction-model-based-on-machine-learning/127130/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this research?","Question",{"text":75,"@type":76},"To improve the accuracy and efficiency of heart disease prediction using machine learning methods for medical diagnosis support.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and features are used in the study?",{"text":80,"@type":76},"The study uses the heart disease dataset from Cleveland UCI, with 14 attributes, and analyzes key health features derived from those attributes.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated?",{"text":84,"@type":76},"Multiple machine learning models are validated using a test set and evaluated with metrics such as accuracy, precision, recall, and F1 score.",{"name":86,"@type":73,"acceptedAnswer":87},"What results do the models achieve, and what is recommended for future work?",{"text":88,"@type":76},"Logistic regression and random forests perform well with high practical value; 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