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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 971272, 16 pages
http://dx.doi.org/10.1155/2013/971272
Research Article

Application of Genetic Algorithm for Discovery of Core Effective Formulae in TCM Clinical Data

1Longhua Hospital Affiliated to Shanghai University of TCM, Shanghai 200032, China
2School of Information Technology, University of Sydney, Sydney, NSW 2006, Australia
3Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong

Received 5 July 2013; Revised 30 August 2013; Accepted 30 August 2013

Academic Editor: Ricardo Femat

Copyright © 2013 Ming Yang 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.

Abstract

Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.