A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques.

This research proposes a novel framework for enhancing heart disease prediction using a hybrid approach that integrates classical and quantum-inspired machine learning techniques. The framework leverages a combined dataset comprising Cleveland, Hungarian, Switzerland, Long Beach, and Statlog datasets, encompassing 1190 observations. After preprocessing and removing 272 duplicate entries, the final dataset consists of 918 unique observations. Data preprocessing has been performed to handle missing values, outliers, and correlations. Feature selection has been employed to identify the most relevant attributes for heart disease prediction. Subsequently, both classical and quantum-inspired models are trained and optimized. The classical models utilized Genetic Algorithms (CGA) and Particle Swarm Optimization (CPSO) for hyperparameter tuning, while the quantum-inspired models employed Quantum Genetic Algorithms (QGAs) and Quantum Particle Swarm Optimization (QPSO). A Support Vector Machine (SVM) classifier has been used in both classical and quantum domains. Tenfold cross-validation has been performed to assess model performance using metrics such as accuracy, F1-score, precision, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio (DOR). The performance of the classical and quantum models has been compared to existing state-of-the-art approaches. The results demonstrated the potential of the proposed hybrid framework in achieving improved heart disease prediction accuracy and robustness.