Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning
Joint Authors
Nanyue, Wang
Youhua, Yu
Dawei, Huang
Bin, Xu
Jia, Liu
Tongda, Li
Liyuan, Xue
Zengyu, Shan
Yanping, Chen
Jia, Wang
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-11-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Nanyue, Wang& Youhua, Yu& Dawei, Huang& Bin, Xu& Jia, Liu& Tongda, Li…[et al.]. 2015. Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1079239
Modern Language Association (MLA)
Nanyue, Wang…[et al.]. Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning. The Scientific World Journal No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1079239
American Medical Association (AMA)
Nanyue, Wang& Youhua, Yu& Dawei, Huang& Bin, Xu& Jia, Liu& Tongda, Li…[et al.]. Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1079239
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1079239