Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

• Published in: Diagnostics (Basel) Vol. 13; no. 6; p. 1099
• Main Authors: Ozpolat, Zeynep, Karabatak, Murat
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2023; MDPI
• Subjects: Algorithms; Arrhythmia; Cardiac arrhythmia; Classification; Computer science; Computers; Datasets; Deep learning; Diagnosis; Electrocardiography; electrocardiography classification; Health aspects; Heart; Machine learning; Methods; Neural networks; Performance evaluation; Principal components analysis; Quantum computing; quantum support vector machine; Sinuses; Software; Support vector machines; Turkey; electrocardiography classification; machine learning; quantum computing; quantum support vector machine
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13061099

The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods have recently come forward. In computer science, machine learning techniques are often preferred for automatic detection. Quantum-based structures have emerged to increase the machine learning algorithm’s speed and classification performance. In this study, a quantum-based machine learning algorithm is applied to classify heart rhythms. The ECG properties were converted to qubit structure using principal component analysis (PCA). The resulting qubits are classified using the quantum support vector machine (QSVM) algorithm. Quantum computer simulation over Qiskit was used for classification studies. Within the scope of experimental studies, comparisons between classical SVM and QSVM were made using different data amounts and qubit numbers. In the results of the analysis, classical SVM achieved 86.96% accuracy, and QSVM achieved 84.64% accuracy. Despite the fact that the entire dataset was not used due to various limitations, these successful performances were achieved. Classification of medical data such as that from ECG has shown that quantum-based machine learning frameworks perform well despite current resource constraints. In this respect, the study includes essential contributions to the use of quantum-based machine learning methods on signal data in medicine.