Comparison of Machine Learning Models for Prediction of In-Hospital Adverse Outcomes after Percutaneous Coronary Intervention

Abstract

Cardiovascular disease is a non-communicable disease that is one of the leading causes of mortality worldwide, and its prevalence is growing year after year. Most cardiovascular diseases require percutaneous coronary intervention (PCI) to remove blood vessel blockages, However, this therapy carries risks of adverse outcomes, such as in-hospital bleeding and mortality. Currently, machine learning is widely used in prediction and decision-making support for clinicians. According to the above reasons, this study demonstrates using and comparing four common machine learning algorithms to predict in-hospital bleeding and in-hospital mortality outcomes. Finally, this study found that the XGBoost model accomplished the most outstanding in-hospital bleeding and mortality results. The performance of in-hospital bleeding shows Accuracy of 0.987 (95% CI: 0.980 - 0.994). In-hospital mortality shows Accuracy of 0.972 (95% CI: 0.962 - 0.982).