Evaluating the Generalizability of Support Vector Machine for Breast Cancer Detection

Breast cancer is caused by the abnormal growth of cells in the breast. Malignant breast cancer is an aggressive type that, if not diagnosed and treated early, can be fatal, While the benign breast cancer can be removed without recurring when detected early. Recent research has shown that Breast cancer detection relies heavily on accurate and reliable methods. Machine learning models, particularly Support Vector Machines (SVMs), have shown promise in this area. However, concerns exist regarding their generalizability across real-world scenarios with varying software environments and data processing techniques. This research proposes to investigate this gap by comparing SVM performance with other classifiers such as Naïve Bayes, Random Forest, Multilayer Perceptron and Decision Tree. These classifiers were tested on the Wisconsin Breast Cancer dataset using both the Waikato Environment for Knowledge Analysis and Jupyter Notebook.