Multiple Granular Analysis of TCM Data with Applications on Diagnosis of Hepatitis B

The objectiveness of Traditional Chinese Medicine (TCM) limits its further development and generalization. Big data provide the golden opportunity for TCM quantization. The main purpose of this paper is to build a bridge between data analysis and clinical experience and provide experimental support for TCM experience. Taking the Hepatitis B disease data as experimental subject, we propose a framework for mining latent relations between features and disease categories based on Granular Computing theory. That is, kmeans clustering and correlation analysis is adopted to analyze the intra-relationship of disease stages and relationship between clinical symptoms and stages respectively. Algorithm based on Latent Dirichlet Allocation model is proposed to mining the mapping relationships of the three layers: clinical symptoms, stages of Hepatitis B and their middle layer syndromes. Experimental results indicate that the results of the data analysis are consistent with the clinical experience of TCM. It is proved that the diagnose based on syndromes is the scientific results of manually mining large amount of history data. Our study is a useful attempt that using data mining techniques to make TCM quantifiable and objective.