{"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":"<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":"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\":\"<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\":\"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}
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.
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.
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