Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome.

ABSTRACT:

Background

Cardiovascular diseases are a significant health burden with the prevalence increasing worldwide. Thus, a highly accurate assessment and prediction of death risk are crucial to meet the clinical demand. This study sought to develop and validate a model to predict in-hospital mortality among patients with the acute coronary syndrome (ACS) using nonlinear algorithms.

Methods

A total of 2414 ACS patients were enrolled in this study. All samples were divided into five groups for cross-validation. The logistic regression (LR) model and XGboost model were applied to predict in-hospital mortality. The results of two models were compared between the variable set by the global registry of acute coronary events (GRACE) score and the selected variable set.

Results

The

SUBMITTER: Li R 

PROVIDER: S-EPMC9933107 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature