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

IF 2.4 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Salam H Bani Hani, Muayyad M Ahmad
{"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":"<p><strong>Purpose: </strong>This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":10832,"journal":{"name":"Current Cardiology Reviews","volume":null,"pages":null},"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}
引用次数: 5

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.

缺血性心脏病预测的机器学习算法:系统综述。
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。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Current Cardiology Reviews
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信