ML based myocardial infarction detection from ECG

Fast and reliable screening of the electrocardiogram helps clinicians catch cardiac problems early. We present a classical machine learning pipeline that separates myocardial infarction from confirmed normal 12-lead ECG recordings. The pipeline builds a compact set of statistical, spectral, wavelet, and heart rate variability features across the twelve leads, then trains a Support Vector Machine and a Random Forest on the PTB-XL corpus. On the held out fold we reach 0.944 accuracy with the SVM and 0.929 accuracy with the Random Forest, with AUROC values of 0.958 and 0.946. The ensemble also exposes which features drive the decision, which supports clinical interpretability. The approach stays light on compute and fits low resource settings where deep networks are hard to deploy.