Improved CAD Classification with Ensemble Classifier and Attribute Elimination

In the realm of medical diagnostics, accurate classification of coronary artery disease (CAD) remains a critical challenge. This research presents a novel approach to enhance CAD classification by integrating an ensemble classifier with the Average-Recursive Attribute Elimination (ARAE) technique. The proposed method aims to optimize feature selection and classification concurrently, addressing the issue of dimensionality while improving classification performance. Experimental results on a comprehensive CAD dataset demonstrate the effectiveness of the approach, showcasing a significant enhancement in classification accuracy compared to conventional methods. This study not only contributes to the field of CAD diagnosis but also highlights the potential of combining ensemble classifiers with advanced feature selection methods for improved accuracy in medical data analysis.