Agung W. Setiawan

Improved accuracy of PCG signal classification for myocardial infarction biomarker using automatic feature selection and boosting process

Myocardial infarction (MI) is a leading global health concern, typically diagnosed using ECG, biomarkers, or imaging, which costly or unavailable in low-resource settings. This study presents a non-invasive, machine learning-based approach using phonocardiogram (PCG) signals for classifying normal, ST-elevation MI (S...