Parham Ghayour*

Sorbonne University, France

*Corresponding Author: Parham Ghayour, Sorbonne University, France.

Received: April 07, 2025; Published: April 29, 2025

Abstract

Breast cancer remains one of the leading causes of mortality worldwide, necessitating accurate and efficient predictive models for early diagnosis. Traditional machine learning approaches have demonstrated high classification accuracy, yet they often struggle with high-dimensional medical data and computational scalability. In this survey, we explore the potential of quantum machine learning (QML) to enhance breast cancer prediction by leveraging quantum computing’s unique properties, such as quantum feature encoding, entanglement-based classifiers, and hybrid quantum-classical learning architectures. We analyze benchmark datasets, evaluate the accuracy trends of classical and quantum models, and discuss challenges such as noisy qubits, barren plateaus in optimization, and dataset compatibility with quantum encodings. Additionally, we provide a comparative analysis of quantum classifiers, highlighting their current performance and limitations in medical diagnosis. While quantum models remain in their early stages, our review identifies promising pathways for future research, including errormitigated quantum kernels, variational quantum circuits tailored for medical data, and quantum generative models for data augmentation. This study serves as a foundational resource for researchers aiming to bridge the gap between classical machine learning and quantum-enhanced predictive modeling in breast cancer diagnostics.

Keywords: Breast Cancer; Machine Learning (ML); Federated Learning (FL)

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Citation

Citation: Parham Ghayour. “A Survey of Classical and Quantum Machine Learning for Breast Cancer Prediction".Acta Scientific Computer Sciences 7.2 (2025): 24-28.

Copyright

Copyright: © 2025 Parham Ghayour. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.