Advanced ML-based ECG Signal Classification for Enhanced Cardiac Diagnosis

Electrocardiogram (ECG) signals play a crucial role in diagnosing heart conditions, particularly myocardial infarction (MI), which remains a leading cause of mortality from cardiovascular diseases. This study introduces a robust framework for MI diagnosis through enhanced ECG signal classification using advanced machine learning (ML) techniques. We employ a carefully selected suite of five prominent ML algorithms-k-nearest neighbors (KNN), random forest (RF), XGBoost, gradient boosting, support vector machine (SVM), and decision tree classifier. Our methodology integrates three strategic feature selection methods: filter, wrapper, and embedded, which collectively enhance the model’s accuracy, discriminative power, and predictive capabilities. The model’s effectiveness is evaluated using benchmark datasets and a thorough set of performance indicators, such as accuracy, precision, recall, F1-score, and Receiver Operating Characteristic (ROC) analysis. Our findings indicate a significant advancement in MI diagnosis and prediction, with particular relevance for neonatal and elderly populations. This research paves the way for a transformative approach in cardiac diagnostics, harnessing cutting-edge ML techniques for precise MI identification. Ultimately, our approach may lead to improved cardiovascular health outcomes and optimized cardiac treatment strategies through the combination of innovative feature selection and a powerful ML framework.