title: Pulse Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Wang
authors.identifier: 0000-0002-9978-6060
authors.initials: W
authors.lastname: Nanyue
authors.affiliation: Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Youhua
authors.affiliation: Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Huang
authors.initials: H
authors.lastname: Dawei
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
authors.forename: Xu
authors.initials: X
authors.lastname: Bin
authors.affiliation: Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Liu
authors.initials: L
authors.lastname: Jia
authors.affiliation: GULOU Hospital of TCM of Beijing, Beijing 100009, China.
authors.forename: Li
authors.initials: L
authors.lastname: Tongda
authors.affiliation: Tai Yang Gong Health Care Center, Chaoyang District, Beijing 100102, China.
authors.forename: Xue
authors.initials: X
authors.lastname: Liyuan
authors.affiliation: Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Shan
authors.initials: S
authors.lastname: Zengyu
authors.affiliation: Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Yanping
authors.affiliation: Shi Xuemin Academician Office, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.
authors.forename: Wang
authors.initials: W
authors.lastname: Jia
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted; D005234:Fatty Liver / Q000175:diagnosis*; D006801:Humans; D008103:Liver Cirrhosis / Q000175:diagnosis*; D000069550:Machine Learning; D008962:Models, Theoretical; D011674:Pulse