Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery.

ABSTRACT:

Background

Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG.

Hyothesis

ML algorithms can significantly improve mortality prediction after CABG.

Methods

Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network

SUBMITTER: Behnoush AH 

PROVIDER: S-EPMC10018097 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature