Machine learning models for classification of myocardial infarction using the PTB-XL dataset
Electrocardiogram (ECG) is an important tool for diagnosing myocardial infarction (MI), but analyzing the ECG signals can sometimes be difficult for physicians. Machine learning could potentially help provide a fast and consistent interpretation of ECG signals. This study aims to evaluate the performance of different machine learning models for classifying MI using ECG data from the PTB-XL dataset