Quantum machine learning‐based framework to detect heart failures in Healthcare 4.0

• Published in: Software, practice & experience practice & experience, 2024-02, Vol.54 (2), p.168-185
• Main Authors: Munshi, Manushi, Gupta, Rajesh, Jadav, Nilesh Kumar, Polkowski, Zdzislaw, Tanwar, Sudeep, Alqahtani, Fayez, Said, Wael
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classifiers; Health care; Heart diseases; Machine learning; Natural language processing; Quantum computing
• ISSN: 0038-0644; 1097-024X
• DOI: 10.1002/spe.3264

Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.