SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for the majority of cancer-related deaths throughout the world. Objectives: The main aim of this study is to diagnose breast cancer at an early stage so that the required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at an early stage using a novel approach that includes an ensemble of the Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. The Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50, respectively. Conclusion: The results prove that the proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors in treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis. © 2021 Bentham Science Publishers.