Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network

Objective: To establish correlation models between various physical examination indexes and traditional Chinese medicine (TCM) constitutions, and explore their relationships based on the radial basis function (RBF) neural network. Methods: The raw data of physical examination indexes and TMC constit...

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Main Authors: Luo Yue, Liu Yu-Nan, Lin Bing, Wen Chuan-Biao
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2020-03-01
Series:Digital Chinese Medicine
Online Access:http://www.sciencedirect.com/science/article/pii/S2589377720300173
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author Luo Yue
Liu Yu-Nan
Lin Bing
Wen Chuan-Biao
author_facet Luo Yue
Liu Yu-Nan
Lin Bing
Wen Chuan-Biao
author_sort Luo Yue
collection DOAJ
description Objective: To establish correlation models between various physical examination indexes and traditional Chinese medicine (TCM) constitutions, and explore their relationships based on the radial basis function (RBF) neural network. Methods: The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned, classified and sorted, on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset. Subsequently, the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions. The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set. Results: Of all selected samples, the highest accuracy rates were 80% for the blood lipid index - TCM constitution model; 100% for the renal function index - TCM constitution model; 100% for the blood routine (male) index - TCM constitution model; 88.8% for the blood routine (female) index - TCM constitution model; 84.1% for the urine routine index - TCM constitution model; and 100% for the blood transfusion index - TCM constitution model. Conclusions: The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions, making it feasible to apply the established correlation models to TCM constitution identification. Keywords: TCM constitution, physical examination index, correlation model, RBF neural network
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spelling doaj.art-6b451ec0db81456cbd2e9a2b7b27203b2022-12-22T03:42:45ZengKeAi Communications Co., Ltd.Digital Chinese Medicine2589-37772020-03-01311119Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural NetworkLuo Yue0Liu Yu-Nan1Lin Bing2Wen Chuan-Biao3School of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, ChinaSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, ChinaAffiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, ChinaSchool of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China; Corresponding author. Research direction: informatization of TCM. .Objective: To establish correlation models between various physical examination indexes and traditional Chinese medicine (TCM) constitutions, and explore their relationships based on the radial basis function (RBF) neural network. Methods: The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned, classified and sorted, on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset. Subsequently, the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions. The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set. Results: Of all selected samples, the highest accuracy rates were 80% for the blood lipid index - TCM constitution model; 100% for the renal function index - TCM constitution model; 100% for the blood routine (male) index - TCM constitution model; 88.8% for the blood routine (female) index - TCM constitution model; 84.1% for the urine routine index - TCM constitution model; and 100% for the blood transfusion index - TCM constitution model. Conclusions: The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions, making it feasible to apply the established correlation models to TCM constitution identification. Keywords: TCM constitution, physical examination index, correlation model, RBF neural networkhttp://www.sciencedirect.com/science/article/pii/S2589377720300173
spellingShingle Luo Yue
Liu Yu-Nan
Lin Bing
Wen Chuan-Biao
Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network
Digital Chinese Medicine
title Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network
title_full Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network
title_fullStr Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network
title_full_unstemmed Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network
title_short Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network
title_sort research on the correlation between physical examination indexes and tcm constitutions using the rbf neural network
url http://www.sciencedirect.com/science/article/pii/S2589377720300173
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AT linbing researchonthecorrelationbetweenphysicalexaminationindexesandtcmconstitutionsusingtherbfneuralnetwork
AT wenchuanbiao researchonthecorrelationbetweenphysicalexaminationindexesandtcmconstitutionsusingtherbfneuralnetwork