Discovering Syndromes in Coronary Heart Disease by Cluster Algorithm Based on Random Neural Network

Integration of western medicine and Traditional Chinese Medicine (TCM) to cure Coronary Heart Disease (CHD) is taken by more and more Chinese. However, the gap between both medical theory systems is still wide. The goal of this contribution is to bridge the gap between them by standardizing syndromes of Traditional Chinese Medicine. We carry out a clinical epidemiology survey of Coronary Heart Disease and obtain 1069 cases. Each case is certainly a CHD case based on the evidence from Coronary Artery Angiography. It includes 78 symptoms and is diagnosed by TCM mentors as syndrome or syndrome combinations. We proposed an unsupervised cluster algorithm to partition 78 symptoms into several clusters. Each cluster is diagnosed by TCM mentor as syndrome and is clinically verified. The obtained seven clusters correspond to seven syndromes in TCM and the clinical verification consolidates the result. Each cluster is used as selected attributes to performe classification and the resulting accuracy is higher than 90%, which indicates that the cluster is successful and the data surveyed is of high quality. The investigation of the cluster algorithm to CHD data to retrieve syndromes in CHD successfully bridges gap between western medicine and TCM.