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Evidence-Based Complementary and Alternative MedicineVolume 2012 2012, Article ID 912852, 14 pages

Research Article

Department of Computer Science and Informatics Institute, University of Missouri, Columbia, MO 65211, USA

MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST-Department of Automation, Tsinghua University, Beijing 100084, China

Received 13 February 2012; Accepted 30 March 2012

Academic Editor: Shi-Bing Su

Copyright © 2012 Ratchadaporn Kanawong 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.


ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

Autor: Ratchadaporn Kanawong, Tayo Obafemi-Ajayi, Tao Ma, Dong Xu, Shao Li, and Ye Duan



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