# Comparison of the Prognostic Performance of Various Machine Learning Models in Patients with Acute Myocardial Infarction: Results from the COREA-AMI Registry

## TL;DR

This study compares machine learning models to predict heart-related risks in patients who had heart attacks and found that the random forest model performed best.

## Contribution

The study evaluates and compares various machine learning models for predicting long-term outcomes in acute myocardial infarction patients who underwent PCI.

## Key findings

- The random forest model outperformed other ML techniques and logistic regression in predicting MACEs.
- Key predictors of MACEs included age, renal function, and adherence to medical therapies.
- The RF model achieved an AUC of 0.822, accuracy of 0.804, and F1 score of 0.870 at 5 years.

## Abstract

Background and Objectives: To date, several machine learning (ML) prognostic prediction models have been investigated for patients with acute myocardial infarction (AMI). However, few studies have compared the prognostic performance of ML techniques in AMI patients who underwent percutaneous coronary intervention (PCI). We sought to compare the prognostic performance among various machine learning techniques to determine which one showed the best prediction ability. Materials and Methods: Using data from the large, multicenter COREA-AMI registry, this study analyzed 10,172 patients to predict major adverse cardiac events (MACEs) at 1 and 5 years. MACE was defined as a composite of cardiac death, myocardial infarction, or cerebrovascular accident. Results: Compared with the four other ML techniques and traditional logistic regression, the random forest (RF) model consistently demonstrated the highest predictive performance. At 5 years, the RF model achieved a superior area under the curve (AUC) of 0.822, an accuracy of 0.804, and an F1 score of 0.870. To ensure clinical interpretability, a SHapley Additive exPlanations analysis was performed on the RF model. It identified key independent predictors for MACEs. The top nonmodifiable predictors included age, renal function, and left ventricular ejection fraction, whereas modifiable risk factors included dual antiplatelet therapy, statin therapy, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker therapy, and adherence to these optimal medical therapy. Conclusions: In this real-world patient cohort, the RF model provided modest improvements in long-term risk stratification, and our findings highlight the continuing importance of guideline-directed medical therapy in determining patient prognosis.