Harnessing Quantum-Classical Techniques for Improved Breast Cancer Prediction
Abstract
Breast cancer is one of the most common neoplasms among women around the world, and early diagnosis significantly improves survival rates. Conventional techniques in the field of machine learning have shown great promise in the prediction of cancer, considering the fact that there is always an opportunity for advancement in terms of computing speed and scalability, especially when working with huge, high-dimensional data sets. In recent years, quantum computing (QC) has seemed to be becoming more prevalent as a powerful alternative. This study investigates the efficiency of traditional and quantum machine learning models in distinguishing between benign and malignant breast cancer tumors. The quantum model has achieved better balance in terms of precision and recall, especially in the case of malignant instance detection. These results demonstrate how QC can be used to supplement conventional machine learning techniques for improved predictive performance in breast cancer diagnosis, marking a significant step toward utilizing the technology’s potential in medicinal applications.
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