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

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. Keywords: TCM constitution, physical examination index, correlation model, RBF neural network