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Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B
Author(s) -
Xiao Yu Chen,
Li Zhuang,
Na Chu,
Min Zhou,
Yiyang Hu
Publication year - 2013
Publication title -
evidence-based complementary and alternative medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.552
H-Index - 90
eISSN - 1741-4288
pISSN - 1741-427X
DOI - 10.1155/2013/695937
Subject(s) - decision tree , feature selection , chronic hepatitis , tree (set theory) , linear discriminant analysis , medicine , artificial intelligence , decision tree learning , machine learning , computer science , selection (genetic algorithm) , pattern recognition (psychology) , data mining , mathematics , immunology , mathematical analysis , virus
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.

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