Quantum‐enhanced recursive feature elimination and physics‐inspired neural networks for coronary artery disease detection

Abstract The early and precise identification of coronary artery disease (CAD) is critical for effective treatment and improved patient outcomes. This study offers a novel approach for enhancing CAD detection by combining quantum computing with advanced neural network techniques. We introduce a quantum‐enhanced recursive feature elimination (QE‐RFE) method that utilizes quantum processing power to optimize feature selection more effectively compared with traditional methods. The QE‐RFE technique is integrated with a physics‐inspired neural network (PINN) that leverages domain‐specific knowledge of cardiovascular physiology and improves diagnostic performance. The PINN framework is constructed based on the physical principles of cardiovascular dynamics and offers a stable and interpretable model for analyzing medical data, yielding enhanced diagnostic accuracy and precision. Experimental results demonstrate that the proposed model achieves an accuracy of 98.26%, significantly outperforming state‐of‐the‐art techniques and demonstrating enhanced computational efficiency. The proposed system represents a significant advancement in CAD detection and has promising clinical applications.