Enhanced Signal Processing for Arrhythmia Detection in ECG: Comparing Various Machine Learning Approaches

Electrocardiogram (ECG) signal classification is important for the early detection of cardiac arrhythmias and the avoidance of life-threatening complications. This work compares several machine learning algorithms—XG Boost, Random Forest, Support Vector Machine (SVM), and Logistic Regression—on benchmark ECG datasets to evaluate their ability to correctly classify cardiac abnormalities. XG Boost produced better results with 0.99 accuracy, precision, recall, and F1-score due to its gradient boosting mechanism, regularization properties, and efficiency in class imbalance handling. Random Forest and SVM came in close second, producing strong and consistent performance with accuracies ranging from 0.97–0.98, and thus, they are appropriate for use in scenarios where real-time analysis and noisy condition generalization are needed. Logistic Regression, though computationally fast, performed below 0.70 accuracy because of its linear nature and inability to handle complex ECG morphology. Comparative evaluation indicated that each model has unique strengths based on the deployment context: XG Boost is best for high-accuracy diagnostics in cloud-based clinical environments, SVM performs best in low-latency real-time wearable devices, and Random Forest is optimal for retrospective ECG analysis and hospital batch processing. Performance metrics and detailed model behaviour were analysed to enable clinical interpretability and trust. In addition, the work suggests a tiered architecture that balances accuracy in diagnosis with computational efficiency through the allocation of lightweight models for primary screening and heavy-duty classifiers for confirmatory diagnosis. These outcomes provide a basis for the integration of AI in highly scalable, resource-conscious cardiac care systems with high reliability and clinical applicability.