Research and Application of Heart Disease Prediction Model Based on Machine Learning

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 learni...

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Main Author: Bao Yongli
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:ITM Web of Conferences
Online Access:https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04023.pdf
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author Bao Yongli
author_facet Bao Yongli
author_sort Bao Yongli
collection DOAJ
description 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.
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issn 2271-2097
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series ITM Web of Conferences
spelling doaj-art-95169ccad72e4f03bb58970f89e6a9712025-02-07T08:21:13ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700402310.1051/itmconf/20257004023itmconf_dai2024_04023Research and Application of Heart Disease Prediction Model Based on Machine LearningBao Yongli0International College, Chongqing University of Posts and TelecommunicationsAs 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.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04023.pdf
spellingShingle Bao Yongli
Research and Application of Heart Disease Prediction Model Based on Machine Learning
ITM Web of Conferences
title Research and Application of Heart Disease Prediction Model Based on Machine Learning
title_full Research and Application of Heart Disease Prediction Model Based on Machine Learning
title_fullStr Research and Application of Heart Disease Prediction Model Based on Machine Learning
title_full_unstemmed Research and Application of Heart Disease Prediction Model Based on Machine Learning
title_short Research and Application of Heart Disease Prediction Model Based on Machine Learning
title_sort research and application of heart disease prediction model based on machine learning
url https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04023.pdf
work_keys_str_mv AT baoyongli researchandapplicationofheartdiseasepredictionmodelbasedonmachinelearning