The classification prognosis models of hepatitis b virus reactivation based on Bayes and support vector machine after feature extraction of genetic algorithm

The purpose of the study is to ascertain the key feature subsets of hepatitis b virus (HBV) reactivation and establish classification prognosis models of HBV reactivation for primary liver carcinoma (PLC) patients after precise radiotherapy (RT). Genetic Algorithm (GA) is proposed to extract the key feature subsets of HBV reactivation from the initial feature sets of primary liver carcinoma. Bayes and support vector machine (SVM) are employed to build classification prognosis models of HBV reactivation, the classification performance of the key feature subsets and the initial feature sets are predicted. The experimental results show that feature extraction based on GA improve the classification performance of HBV reactivation, five risk factors have best recognition performance of HBV reactivation, including 'HBV DNA level', 'tumor staging TNM', 'outer margin of radiotherapy', 'two kinds code of outer margin of radiotherapy' and 'V45'. Two kinds of classifiers have good recognition performance in HBV reactivation. The best classification accuracy of Bayes classifier reached to 82.07%, and the best classification accuracy of SVM classifier reached to 82.89%.