Comparative Analysis of Classical and Quantum Machine Learning for Breast Cancer Classification
Manaswini De, Arunima Jaiswal, Tanya Dixit · 2024
Breast cancer, the unruly development of abnormal cells in breasts, is a pressing public health issue and needs early detection for improving survival rates. Machine learning algorithms have shown promising results in breast cancer classification and detection. This study evaluates the performances of traditional and quantum machine learning techniques for data classification of breast cancer, emphasizing their effectiveness in discerning patterns using reduced set of features. An approach is proposed to reduce 30-dimensional features to 2-dimensional features using widely applied Principal Component Analysis (PCA), Incremental PCA, Truncated Singular Value Decomposition and Kernel PCA techniques. This study aims to provide an insight on the impact of reduction of features on the classification of breast cancer using classical and quantum machine learning techniques.