An improved approach to the classification of seven common TCM pulse conditions

Ma, Chang; Xia, Chunming; Wang, Yiqin; Yan, Hong‐Bin; Li, Fufeng

doi:10.1109/bmei.2011.6098423

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Ling et al compared various neural networks with the improved Echo State Network (ESN), which is based on the chaos theory, validated the effectiveness, and superiority of ESN neural network. Ma et al [ 76 ] proposed an improved two-step classification method to classify seven common pulse patterns. They first compared eight discriminant functions including SVM, k -NN, and decision tree.…”

Section: Machine Learning Approaches For Tcm Patient Classificatiomentioning

confidence: 99%

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Zhao

Wang

et al. 2015

Evidence-Based Complementary and Alternative Medicine

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As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

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Section: Machine Learning Approaches For Tcm Patient Classificatiomentioning

confidence: 99%

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Zhao

Wang

et al. 2015

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…At present, many scholars have applied biology knowledge and machine learning algorithm to TCM diagnosis process [12–16]. Zhao et al [17] discussed the research of machine learning and TCM diagnosis so as to further study the classification of patients.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

Huan

Wen

Zhang

et al. 2017

Computational and Mathematical Methods in Medicine

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Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

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A Pulse Condition Reproduction Apparatus for Remote Traditional Chinese Medicine

Liu

Liang

et al. 2018

Intelligent Robotics and Applications

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An improved approach to the classification of seven common TCM pulse conditions

Cited by 4 publications

References 9 publications

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

A Pulse Condition Reproduction Apparatus for Remote Traditional Chinese Medicine

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