A data-driven principal component analysis-support vector machine approach for breast cancer diagnosis: Comparison and application

In recent years, with the development of artificial intelligence, data-driven methodologies have been widely studied in fault diagnosis and detection, since an increasing number of complexities of modern complex systems make the mechanism model information difficult to obtain. Especially in people’s health monitoring, it is very difficult to achieve the mechanism model. The existing challenges, such as huge amount of data, high data dimension, large noise interference, and so forth, make the applications of data-driven approaches more suitable. For the sake of solving the problems above, we present principal component analysis-support vector machine (PCA-SVM) method with different kernels to reduce data dimension, and two sets of breast-cancer data are utilized to verify the method. Additionally, support vector machine-recursive feature elimination (SVM-RFE), the original SVM with different kernels, PCA and modified PCA (MPCA) methods are also applied to diagnose malignant cancer in comparison with PCA-SVM. In experiments, PCA-SVM via radial basis function (RBF) kernel shows better performance than other methods, with the two breast cancer datasets obtained from the University of Wisconsin Hospital. Finally, PCA-SVM in this study uses only six principal components and obtains better accuracy (97.19%) than most of the previous studies.