Do Quantum Machine Learning models improve the classification of cardiovascular diseases from ECG images compared to classical machine learning models?
Quantum Machine Learning models, particularly Quanvolutional Neural Networks, demonstrate high accuracy in classifying cardiovascular diseases from ECG images, significantly outperforming classical machine learning approaches.
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 the other models by achieving 97.31% accuracy, 97.41% precision, 97.31% recall, 97.30% F1 score, and 99.10% specificity.
Prabhu et al. (Sun,) studied this question.