Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction

Abstract

This study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.