Breast Cancer Detection Using Reduced Feature Representation

Breast cancer remains one of the most common causes of mortality among women worldwide. Accurate and early diagnosis plays an essential role in improving survival rates. Machine learning techniques have increasingly been applied in medical decision-support systems to assist physicians in identifying malignant tumors more reliably. This research investigates the use of Independent Component Analysis (ICA) as a feature reduction technique for breast cancer classification. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset is utilized, which initially contains thirty diagnostic attributes extracted from digitized biopsy images. ICA is applied to trans- form the original feature space into a reduced representation consisting of a single independent component. To evaluate the effectiveness of this dimensionality reduction approach, several machine learning classifiers are employed, including k-Nearest Neighbor (k-NN), Artificial Neural Networks (ANN), Radial Basis Function Neural Networks (RBFNN), and Support Vector Machines (SVM). The classification performance is examined using both the original 30-feature dataset and the reduced feature representation. Different validation strategies such as 5- fold cross-validation, 10-fold cross-validation, and random data partitioning are used to assess performance. The classifiers are evaluated using multiple metrics including accuracy, sensitivity, specificity, F-score, Youden’s index, discriminant power, and Receiver Operating Characteristic (ROC) analysis. Experi- mental results indicate that reducing the feature dimension through ICA significantly decreases computational cost while maintaining competitive diagnostic accuracy. These findings suggest that ICA-based feature reduction can be beneficial for developing efficient computer-aided breast cancer diagnosis systems.