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The Scientific World Journal
Volume 2015 (2015), Article ID 859192, 9 pages
http://dx.doi.org/10.1155/2015/859192
Research Article

Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning

1Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
3GULOU Hospital of TCM of Beijing, Beijing 100009, China
4Tai Yang Gong Health Care Center, Chaoyang District, Beijing 100102, China
5Shi Xuemin Academician Office, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China

Received 21 January 2015; Revised 28 May 2015; Accepted 15 November 2015

Academic Editor: Zhaohui Liang

Copyright © 2015 Wang Nanyue 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

Objective. To compare the signals of pulse diagnosis of fatty liver disease (FLD) patients and cirrhosis patients. Methods. After collecting the pulse waves of patients with fatty liver disease, cirrhosis patients, and healthy volunteers, we do pretreatment and parameters extracting based on harmonic fitting, modeling, and identification by unsupervised learning Principal Component Analysis (PCA) and supervised learning Least squares Regression (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation step by step for analysis. Results. There is significant difference between the pulse diagnosis signals of healthy volunteers and patients with FLD and cirrhosis, and the result was confirmed by 3 analysis methods. The identification accuracy of the 1st principal component is about 75% without any classification formation by PCA, and supervised learning’s accuracy (LS and LASSO) was even more than 93% when 7 parameters were used and was 84% when only 2 parameters were used. Conclusion. The method we built in this study based on the combination of unsupervised learning PCA and supervised learning LS and LASSO might offer some confidence for the realization of computer-aided diagnosis by pulse diagnosis in TCM. In addition, this study might offer some important evidence for the science of pulse diagnosis in TCM clinical diagnosis.