数字中医药（英文）2020，Vol.3Issue(1)：11-19,9.DOI:10.1016/j.dcmed.2020.03.002
基于RBF神经网络的体检指标与中医体质关联性研究
Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network
摘要
Abstract
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.关键词
中医体质/体检指标/关联模型/RBF神经网络Key words
TCM constitution/Physical examination index/Correlation model/RBF neural network引用本文复制引用
罗悦,刘玉楠,林冰,温川飙..基于RBF神经网络的体检指标与中医体质关联性研究[J].数字中医药（英文）,2020,3(1):11-19,9.基金项目
We thank for the funding support from the National Key Research and Development Project of China (No. 2018YFC1707606), National Natural Science Foundation of China (No. 81904324) and Youth Foundation of Sichuan Administration of Traditional Chinese Medicine (No. 2016Q065). （No. 2018YFC1707606）
