Quantum‐enhanced recursive feature elimination and physics‐inspired neural networks for coronary artery disease detection
Year: 2026
Type: article
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.
Source: ETRI Journal
Authors: Kajeeth Kumar G, Muthurajkumar S
Institution: Anna University, Chennai
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7160854552