A Breast Cancer Detection Model using a Tuned SVM Classifier

Breast cancer has become a common disease that affects women all over the world. Early detection and diagnosis of the breast cancer is crucial for an effective medication and treatment. But, detection of breast cancer at the primary stage is challenging due to the ambiguity of the mammograms. Many researchers have explored Machine learning (ML) based model to detect breast cancer. Most of the developed models have not been clinically effective. To address this, in this paper, we propose an optimized SVM based model for the prediction of breast cancer where Bayesian search method is applied to discover the best hyper-parameters of the SVM classifier. Performance of the model with default hyper-parameter for the SVM is compared to the performance with tuned hyper-parameter. The comparison shows that performance is significantly improved when the tuned hyper-parameter is used for training SVM classifier. Our findings show that SVM’s performance with default parameters is 96% whereas the maximum accuracy level 98% is obtained using tuned hyper-parameter.