缺血性心脏病预测的机器学习算法：系统综述。

IF 2.4 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Current Cardiology Reviews Pub Date : 2023-01-01 DOI:10.2174/1573403X18666220609123053

Salam H Bani Hani, Muayyad M Ahmad

{"title":"缺血性心脏病预测的机器学习算法：系统综述。","authors":"Salam H Bani Hani, Muayyad M Ahmad","doi":"10.2174/1573403X18666220609123053","DOIUrl":null,"url":null,"abstract":"Purpose: This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.Methods: This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\\ MEDLINE, CINAHL, and IEEE explore.Results: Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning.Conclusion: Applying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations.","PeriodicalId":10832,"journal":{"name":"Current Cardiology Reviews","volume":"19 1","pages":"e090622205797"},"PeriodicalIF":2.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10201879/pdf/","citationCount":"5","resultStr":"{\"title\":\"Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.\",\"authors\":\"Salam H Bani Hani, Muayyad M Ahmad\",\"doi\":\"10.2174/1573403X18666220609123053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose: This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.Methods: This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\\\\ MEDLINE, CINAHL, and IEEE explore.Results: Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning.Conclusion: Applying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations.\",\"PeriodicalId\":10832,\"journal\":{\"name\":\"Current Cardiology Reviews\",\"volume\":\"19 1\",\"pages\":\"e090622205797\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10201879/pdf/\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Cardiology Reviews\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/1573403X18666220609123053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Cardiology Reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1573403X18666220609123053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}

引用次数: 5

摘要

目的：本综述旨在总结和评估用于预测缺血性心脏病的最准确的机器学习算法。方法：根据PRISMA指南进行系统回顾。使用Science Direct、PubMed\MEDLINE、CINAHL和IEEE explore等多个数据库进行了全面搜索。结果：2017年至2021年间发表的13篇文章符合入选条件。提取了三个主题：预测缺血性心脏病的常用算法，预测缺血性心脏疾病的算法的准确性，以及提高护理质量的临床结果。所有方法都使用了有监督和无监督的机器学习。结论：应用机器学习有望帮助临床医生解释患者的数据并为其数据集实现最佳算法。此外，机器学习可以建立基于证据的基础，支持医疗保健提供者管理需要导管等侵入性程序的个人情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.

Purpose: This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.

Methods: This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\ MEDLINE, CINAHL, and IEEE explore.

Results: Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning.

Conclusion: Applying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Current Cardiology Reviews CARDIAC & CARDIOVASCULAR SYSTEMS-

CiteScore

3.70

自引率

10.50%

发文量

117

期刊介绍： Current Cardiology Reviews publishes frontier reviews of high quality on all the latest advances on the practical and clinical approach to the diagnosis and treatment of cardiovascular disease. All relevant areas are covered by the journal including arrhythmia, congestive heart failure, cardiomyopathy, congenital heart disease, drugs, methodology, pacing, and preventive cardiology. The journal is essential reading for all researchers and clinicians in cardiology.