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

ABSTRACT:

Background

The development of prognostic models for the identification of high-risk myocardial infarction (MI) patients is a crucial step toward personalized medicine. Genetic factors are known to be associated with an increased risk of cardiovascular diseases; however, little is known about whether they can be used to predict major adverse cardiac events (MACEs) for MI patients. This study aimed 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 prognostic power of the model.

Methods

We analyzed the data from 218 MI patients admitted to the emergency department at the Surgut District Center for Diagnostics and Cardiovascular Surgery, Russia. Upon a

SUBMITTER: Kirdeev A 

PROVIDER: S-EPMC11410707 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature