QuCardio: Application of Quantum Machine Learning for Detection of Cardiovascular Diseases

This research is the first of its kind to leverage the power of Quantum Machine Learning (QML) to perform multi-class classification of Cardiovascular Diseases (CVDs). We propose a novel approach that enables multi-class classification with Pegasos Quantum Support Vector Classifier (QSVC). The QSVC and the Pegasos QSVC significantly outperform the classical SVC by a margin of +10.76% and +9.72%, respectively. The paper further ventures into a quantum deep learning based architecture with a novel Quanvolutional Neural Network (QNN) implementation, outperforming not only its classical CNN counterpart by +3.88% but also the other models by achieving 97.31% accuracy, 97.41% precision, 97.31% recall, 97.30% F1 score, and 99.10% specificity.