AUTHOR=Kirdeev Alexander , Burkin Konstantin , Vorobev Anton , Zbirovskaya Elena , Lifshits Galina , Nikolaev Konstantin , Zelenskaya Elena , Donnikov Maxim , Kovalenko Lyudmila , Urvantseva Irina , Poptsova Maria 

TITLE=Machine learning models for predicting risks of MACEs for myocardial infarction patients with different VEGFR2 genotypes

JOURNAL=Frontiers in Medicine

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1452239

DOI=10.3389/fmed.2024.1452239

ISSN=2296-858X

ABSTRACT=Background: Development of prognostic models for identification of high-risk myocardial infarction (MI) patients is a crucial step towards personalized medicine. Genetic factors are known to be associated with increased risk of cardiovascular diseases; however, little is known if they can be used to predict major adverse cardiac events (MACEs) for MI patients. This study aims to build a machine learning (ML) model to predict MACEs in MI patients based on clinical, imaging, laboratory and genetic features, and to assess the influence of genetics on the model's prognostic power.We analyzed the data from 218 MI patients admitted to the emergency department at the