TCM syndrome classification using graph convolutional network

Traditional Chinese Medicine (TCM) diagnosis is a reasoning process through expert knowledge, in which syndrome classification is a key step for prescription recommendation and the treatment of patients. Doctors generally differentiate syndrome types according to patients' symptoms and state elements. This paper proposes a syndrome classification method based on graph convolutional network with residual structure, to exploit the potential relationship between symptoms and state elements. We constructed a graph convolutional network by combining symptoms and state elements for syndrome classification, called Symptoms-State elements Graph Convolutional Network (SSGCN), embedding the inherent logic of TCM diagnosis and treatment with a prescription graph. This graph architecture wherein contained the relationship between symptoms and state elements, and a multi-layer perceptron (MLP) was trained to classify different syndromes. Experiments were conducted on two self-built datasets according to two classic TCM books, i.e., Theories on Febrile Diseases and Traditional Chinese Medicine Prescription Dictionary. Accuracy, precision, recall and F1-score were adopted to evaluate the syndrome classificaiton results. Our proposed SSGCN method achieved accuracy of 75.59%, 69.63%, precision of 69.10%, 76.33%, recall of 75.63%, 66.67% and F1-score of 71.26%, 65.84% in the above two datasets, respectively. The proposed method for syndrome classification outperformed several popular methods including support vector machine, random forest, extreme gradient boosting and convolutional neural network. By constructing a prescription graph in which symptoms are used as nodes and state elements are taken into account for edges, graph convolution is implemted to capture the relationship of symptoms and state elements. This model improves the performance of syndrome classification and can be further extened for some other related applications in TCM.