Machine learning for early prediction of in‐hospital cardiac arrest in patients with acute coronary syndromes

Abstract

The XGBoost model, which was generated based on a machine learning algorithm, has high potential to be used to predict cardiac arrest in ACS patients. This proposed model significantly improves outcome prediction compared to existing risk prediction scores.