Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients.

ABSTRACT: Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression

SUBMITTER: Juan-Salvadores P 

PROVIDER: S-EPMC8870965 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature