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.