Quantum Machine Learning for Non-Communicable Disease Classification: A Comprehensive Review
S. Poongothai, R. Senthil Kumar
Quantum computing (QC) has emerged as a revolutionary technology that can solve computationally rigid problems significantly faster than classical computers, potentially facilitating the development of Machine Learning (ML). This paper provides a deeper review of Quantum Machine Learning (QML), covering basic principles, quantum-classical techniques and real-world healthcare applications. The review paper evaluates the latest developments in quantum-enhanced ML models, including quantum support vector (QSVMs), quantum neural networks (QNNs), quantum principal component (QPCAs), quantum convolutional neural networks (QCNNs) and quantum K-nearest neighbor (QKNNs) techniques. Additionally, the review paper discusses complex challenges such as low computational power, huge, overfitting risk, high-dimensional and noisy datasets in classical ML techniques. Finally, the review paper summarizes the effectiveness of QML techniques compared with classical ML classification techniques based on their accuracy values. This review serves as a comprehensive resource for researchers exploring the classification of various non-communicable diseases (NCDs) on the intersection of quantum computing and machine learning.
International Conference for Advancement in Technology (ICONAT)•Sep 19, 2025•0 citations