Application of Support Vector Machines Kernel Functions for Breast Cancer Detection and Classification

This study deals with the linear, polynomial, and radial basis function (RBF) kernel-based support vector machines (SVM) applied to the classification of breast cancer. It checks the efficiency of different kernel-based techniques for distinguishing benign and malignant tumors against medical datasets. This analysis is conducted on the public Wisconsin Breast Cancer Dataset borrowed from the UCI Machine Learning Repository, which consists of 569 cases, 357 benign and 212 malignant. It aims to develop the assessment necessary for each Kernel based on its accuracy, precision, recall, computational efficiency, F1 score, and area under the curve (AUC). The outcome reveals that, in general, the polynomial Kernel will provide top-ranking results across the various features, thereby enhancing accuracy (96.84%), specificity (97.76%), and AUC (0.9966) compared to the linear and RBF kernels for use in disease classifications of cancers. These findings indicate that the polynomial kernel adeptly captures complex, non-linear data relationships, making it a strong candidate for developing more accurate SVM-based breast cancer detection models.