{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["19(1)"],"submitter":["Bani Hani SH"],"pubmed_abstract":["<h4>Purpose</h4>This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>Applying machine-learning is expected to assist cl"],"journal":["Current cardiology reviews"],"pagination":["e090622205797"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10201879"],"repository":["biostudies-literature"],"pubmed_title":["Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review."],"pmcid":["PMC10201879"],"pubmed_authors":["Bani Hani SH","Ahmad MM"],"additional_accession":[]},"is_claimable":false,"name":"Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.","description":"<h4>Purpose</h4>This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>Applying machine-learning is expected to assist cl","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2025-04-19T14:32:18.399Z","creation":"2025-04-19T14:32:18.399Z"},"accession":"S-EPMC10201879","cross_references":{"pubmed":["35692135"],"doi":["10.2174/1573403X18666220609123053"]}}