Integrating quantum machine learning and deep learning for the classification of cardiovascular diseases

Quantum computing is fast-growing and progressive technology that provides the best results in various domains – including healthcare. In this paper, the Quantum Machine Learning (QML) algorithms, namely Quantum Support Vector Classifiers (QSVC) and Quantum Convolutional Neural Networks (QCNN), are employed for classifying cardiovascular diseases using the Cardiovascular dataset from Kaggle. In contrast with conventional models, the QML algorithms make explicit use of superposition and entanglement to compress and process highly complex and high-dimensional data. In the QSVC approach, patient data are transformed into quantum space with higher-dimensional features by quantum feature maps. This transformation aids QSVC in the process of identifying the complex patterns which are missed in traditional ML methods. In QCNN, the quantum circuits are combined with convolutional layers to capture the understandable features and improve its ability in classification. Both methods are deployed on performance metrics such as Accuracy, Precision, Recall, F1-score and AUC-ROC to show the potential of the methods. The accuracy of the produced QCNN (92%) is greater than that of QSVC (87%). The quantum models demonstrate superior performance regarding metrics and precision level as a result of their rapid convergence. A medical diagnostic revolution occurs through QML algorithms enabling classification between healthy and unhealthy cardiovascular patterns.