Predictive Modeling of Cardiac Arrhythmias Using Hybrid Feature Selection and Ensemble Learning Techniques

Abstract

Cardiac arrhythmias pose a significant threat to global health, necessitating accurate and timely diagnosis for effective treatment. This research investigates the application of hybrid feature selection techniques combined with ensemble learning methods to improve the predictive accuracy of cardiac arrhythmia classification. We propose a novel approach that integrates filter-based (Information Gain) and wrapper-based (Genetic Algorithm) feature selection to identify the most relevant electrocardiogram (ECG) features. These selected features are then utilized to train various ensemble models, including Random Forest, Gradient Boosting Machines (GBM), and XGBoost. The performance of these models is evaluated using a comprehensive dataset of ECG recordings, and the results demonstrate a significant improvement in classification accuracy, precision, recall, and F1-score compared to traditional machine learning approaches. The proposed methodology offers a robust and efficient solution for cardiac arrhythmia prediction, potentially aiding clinicians in early diagnosis and personalized treatment planning.