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Evidence-Based Complementary and Alternative Medicine
Volume 2013 (2013), Article ID 695937, 9 pages
Research Article

Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B

1Centre of Traditional Chinese Medicine Information Science and Technology, Shanghai University of T.C.M., Cailun Road 1200, Shanghai 201203, China
2Department of Computer Science, Shanghai Jiaotong University, Shanghai 200240, China
3Research Institute of Liver Diseases, Shuguang Hospital, Shanghai 201203, China

Received 19 October 2012; Revised 28 December 2012; Accepted 29 December 2012

Academic Editor: William Cho

Copyright © 2013 Xiao Yu Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.