The application of feature selection in Hepatitis B virus reactivation

Abstract

This article aims at finding the risk factors for hepatitis B virus (HBV) reactivation after the precise radiotherapy in patients with primary liver cancer (PLC). We use sequential forward selection and sequential backward selection to extract features which would be combined into an optimal feature subset, and then establish Bayesian and support vector machine (SVM) classification model. We use sequential forward selection to select the key features and find that the AFP, HBV DNA levels, outer margin of radiotherapy, times of radiotherapy, split method were the risk factors of HBV reactivation. The accuracy of the key features with Bayesian classifier reached to 80.65% by using 5 fold cross validation and with SVM reached to 79.84% by using 10 fold cross validation. Besides, we use sequential backward selection find that KPS score, HBV DNA level, outer margin of radiotherapy, tumor stage TNM, equivalent biometric were the risk factors of HBV reactivation, meanwhile, the accuracy of Bayesian classifier can be reached to 85.77% by using fold cross validation and with SVM classifier can be reached to 87.31% by using 10 fold cross validation. The accuracy of original feature with Bayesian classifier reached to 71.42% by using 10 fold cross validation and with SVM classifier reached to 78.10% by using 5 fold cross validation. The experimental results showed that the key feature subset has a better classification performance than the initial feature set clearly.