Enhanced Feature Selection Using Quantum-Inspired Cuckoo Search and Machine Learning for Heart Disease Prediction

Abstract

Heart disease remains a leading global health challenge demanding accurate predictive models for early diagnosis. Traditional machine learning (ML) models struggle with high-dimensional data, feature selection, and interpretability in clinical settings. To address these challenges, we propose a Quantum-Inspired Cuckoo Search Feature Selection Algorithm (QICSFA) integrating quantum principles for optimized feature selection. Experimental results show that QICSFA combined with Bayesian Optimization (BO) achieves 97% accuracy with XGB and 96% with RF by outclassing conventional methods. The key features such as maximum heart rate (Thalach), chest pain type (Cp), and ST depression (Oldpeak) align with known cardiovascular risk factors to ensure clinical relevance. In the future, this study establishes QICSFA as a scalable AI-driven diagnostic tool with potential applications in real-time patient monitoring, multi-institutional dataset validation, and explainable AI (XAI) integration, enhancing trust and adoption in healthcare systems.