DEEP LEARNING APPROACHES FOR CARDIAC DISEASE DIAGNOSIS USING MULTI-MODAL INTEGRATION

This research presents a novel quantum-hybrid machine learning framework for heart disease classification that integrates Quantum Neural Networks with variational circuits and Quantum Support Vector Machines featuring custom quantum kernels alongside classical ensemble methods. The methodology employs advanced feature engineering, multi-strategy missing value imputation, and combined feature selection techniques on an enhanced dataset of 2,000 cardiovascular cases with 17 engineered features. The quantum components utilize 8-10 qubits across 5-6 variational layers with quantum interference effects for enhanced pattern recognition capabilities. Through weighted soft voting ensemble strategy and dynamic threshold optimization, the quantum-hybrid system achieved exceptional performance metrics including 96.2% accuracy, 95.8% F1-score, 94.7% precision, and 96.9% recall, significantly exceeding the 95% accuracy target. The quantum models demonstrated measurable advantages over classical approaches, with the Quantum Neural Network reaching 92.4% accuracy and Quantum SVM achieving 90.8% accuracy independently. The hybrid ensemble showed 4.3 percentage points improvement over the best individual classical model and 2.7 percentage points over standalone quantum implementations. This quantum-hybrid approach establishes superior diagnostic capabilities for cardiovascular disease prediction, particularly in minimizing false negatives critical for patient safety, positioning quantum-enhanced machine learning as a viable solution for next-generation medical diagnostic systems with clinically relevant accuracy improvements for healthcare applications.