Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label

Jin Xu; Zhao-xia Xu; Ping Lu; Rui Guo; Hai-xia Yan; Wen-jie Xu; Yi-qin Wang; Chun-ming Xia

doi:10.1007/s11655-016-2264-0

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Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label

OriginalPaper | Updated：2021-08-27

- Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label
- Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label
- 中国结合医学杂志（英文版） 2016年22卷第11期页码：867-871
- Affiliations：
  
  1. School of Basic Medicine, Shanghai University of Traditional Chinese Medicine,Shanghai,China
  2. Center for Mechatronics Engineering, East China University of Science and Technology,Shanghai,China
- Author bio：
- Funds：
  
  Supported by the National Natural Science Foundation of China (No. 81173199), Shanghai Sailing Program (No.15YF1412100), Young Teachers’ Training Funded Project in Shanghai University (No. ZZszy13003) and Budget for Research Shanghai Municipal Education Commission (No. 2013JW06), Chin
- DOI：10.1007/s11655-016-2264-0
  中图分类号：
- 纸质出版日期：2016，
  
  网络出版日期：2016-10-26，
Scan for full text
Xu, J., Xu, Zx., Lu, P. et al. Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label., Chin. J. Integr. Med. 22, 867–871 (2016). https://doi.org/10.1007/s11655-016-2264-0

Jin Xu, Zhao-xia Xu, Ping Lu, et al. Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label[J]. Chinese Journal of Integrative Medicine, 2016,22(11):867-871.
Xu, J., Xu, Zx., Lu, P. et al. Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label., Chin. J. Integr. Med. 22, 867–871 (2016). https://doi.org/10.1007/s11655-016-2264-0 DOI：

Jin Xu, Zhao-xia Xu, Ping Lu, et al. Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label[J]. Chinese Journal of Integrative Medicine, 2016,22(11):867-871. DOI： 10.1007/s11655-016-2264-0.

摘要

To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems

a tongue diagnosis instrument

a ZBOX-I pulse digital collection instrument

and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). REAL was employed to establish a Xin (Heart) qi defificiency

Xin yang defificiency

Xin yin defificiency

blood stasis

and phlegm fifive-card CM diagnostic model

which had recognition rates of 80.32%

89.77%

84.93%

85.37%

and 69.90%

respectively. The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM

i.e.

observation

auscultation and olfaction

inquiry

and pulse diagnosis

can be characterized by these parameters

which is consistent with CM theory.

Abstract

a tongue diagnosis instrument

a ZBOX-I pulse digital collection instrument

Xin yang defificiency

Xin yin defificiency

blood stasis

and phlegm fifive-card CM diagnostic model

which had recognition rates of 80.32%

89.77%

84.93%

85.37%

and 69.90%

i.e.

observation

auscultation and olfaction

inquiry

and pulse diagnosis

can be characterized by these parameters

which is consistent with CM theory.

关键词

Chinese MedicineSyndrome Differentiationmulti-label learning algorithm

Keywords

Chinese MedicineSyndrome Differentiationmulti-label learning algorithm

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