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- Title
基于遗传算法特征选择的 HBV 再激活分类预测模型.
- Authors
吴冠朋; 刘毅慧; 王 帅; 黄 伟; 刘同海; 尹 勇
- Abstract
This study investigates the risk features and classification prognosis models for hepatitis b virus (HBV) reactivation in patients with primary liver carcinoma after precise radiotherapy (RT). Feature selection method based on Genetic Algorithm (GA) is proposed,the optimal feature subsets are selected from initial feature sets of primary liver carcinoma. HBV reactivation classification prediction models of Bayes and support vector machine (SVM) are built,and the models are used to evaluate predict the classification performance of the optimal feature subsets and initial feature sets. The experimental results show that feature selection based on GA improved the classification performance of HBV reactivation, and the classification performance of the optimal feature subset is much better than the initial features set. The optimal feature subset affecting HBV reactivation include HBV DNA level,tumor staging TNM,Child-Pugh,outer margin of RT and maximum dose of liver. The classification accuracy of Bayes is up to 82.89%,and the classification accuracy of SVM is up to 83.34%.
- Publication
Chinese Journal of Bioinformatics, 2016, Vol 14, Issue 4, p243
- ISSN
1672-5565
- Publication type
Academic Journal
- DOI
10.3969/j.issn.1672-5565.2016.04.08