CLASSIFICATION SUPPORT VECTOR MACHINE IN BREAST CANCER PATIENTS

Abstract

Support vector machine is one of the supervised learning methods in machine learning that is used in classification. The purpose of this study is to measure the accuracy of classification by using 3 hyperplane functions in SVM, namely linear, sigmoid, polynomial, and radial basis function (RBF). Based on the simulation results of training data and testing data on female breast cancer patients, SVM with hyperplane RBF has better accuracy than hyperplane polynomial, linear and sigmoid. The RBF results for the training and testing data were 89.1% and 73.2%, respectively. Based on the results of the classification of training data for female breast cancer patients, 88.07% had no recurrence and 93.33% had recurrence events. Meanwhile, based on the results of the classification of testing data, female patients did not recurrence events and recurrence events was 72.55% and 80.00%, respectively. So from this article, it can be concluded that SVM with hyperplane RBF is one of the best methods in the application of the method of classifying female breast cancer patients.