Feature Selection-Assisted Support Vector Machine Classification of Breast Cancer Mammographic Outcomes
Beryl Kanali *
Multimedia University of Kenya, Nairobi, Kenya.
Pius Kihara
Technical University of Kenya Nairobi, Kenya.
Anthony Karanjah
Multimedia University of Kenya, Nairobi, Kenya.
*Author to whom correspondence should be addressed.
Abstract
Aims: This study assessed the effect of feature selection on Support Vector Machine classification of breast cancer mammographic outcomes using image-derived mammographic predictors.
Study Design: The study adopted a secondary-data comparative statistical learning design.
Place and Duration of Study: The study used the publicly available Curated Breast Imaging Subset of the Digital Database for Screening Mammography. The analysis was conducted at the Department of Mathematics, Multimedia University of Kenya.
Methodology: After image matching and preprocessing, 2,863 training observations and 704 testing observations were retained. Thirty-four image-derived predictors were extracted from cropped mammogram images and region of interest masks. The predictors comprised texture, shape and intensity features. Correlation-Based Feature Selection and Elastic Net-regularised multinomial logistic regression were applied before radial basis function Support Vector Machine classification. Model performance was assessed using accuracy, precision, recall, macro F1-score, weighted F1-score and McNemar’s test.
Results: Correlation-Based Feature Selection retained five predictors, reducing the feature space by 85.3%. Elastic Net retained sixteen predictors after coefficient ranking and cross-validated subset selection, reducing the feature space by 52.9%. Elastic Net-SVM achieved the highest accuracy of 0.5369, while the baseline SVM trained on all extracted predictors achieved the highest macro F1-score of 0.5200. McNemar’s tests showed no statistically significant differences in paired classification error rates among the three SVM models at the 5% level.
Conclusion: Feature selection reduced the input feature space and improved model interpretability but did not produce a statistically significant improvement in overall SVM classification performance. The findings highlight the need to report class-level performance, particularly malignant recall, when evaluating feature selection-assisted mammographic classification models.
Keywords: Breast cancer, mammography, feature selection, correlation-based feature selection, elastic net, support vector machine, mammographic outcomes