Title: The ultimate kernel machine for diagnosis of breast cancer
Authors: Pooja J. Shah; Trupti P. Shah
Addresses: Department of Applied Mathematics, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India ' Department of Applied Mathematics, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India
Abstract: In this paper, an extensive study of diagnosis of breast cancer is made using support vector machine (SVM) technique. To build the cost-effective kernel machine for breast cancer diagnosis, the tools of principal component analysis (PCA) and k-fold cross-validation (CV) techniques are employed. The model is implemented on WDBC and WBC datasets to check the condition of the tumour for its malignancy. Classification accuracy and computation time are obtained and comparative experimental results are analysed under different conditions. For WBC dataset, 100% accuracy is obtained using polynomial kernel in just 0.03 second.
Keywords: breast cancer; support vector machine; SVM; principal component analysis; PCA; k-fold cross validation; CV.
International Journal of Applied Pattern Recognition, 2022 Vol.7 No.1, pp.1 - 14
Received: 08 Jun 2020
Accepted: 16 Mar 2021
Published online: 14 Apr 2022 *