Abstract
Selection of treatment according to the differentiation of syndromes is the kernel theory of traditional Chinese medicine. Different symptoms play different roles in the differentiation of syndromes. It is important to know the contribution rate of each symptom quantitatively. How to find the most informative symptoms combination served as the differential diagnosis standard of a syndrome is also a significant problem in traditional Chinese medicine too. Mutual information can measure arbitrary statistical dependence between variables. Features selection based on mutual information has been widely applied. In this paper, a new definition of contribution rate based on mutual information is proposed. In the definition, joint mutual information needs some form of calculation. An efficient calculation method of joint mutual information and a novel features selection method are developed. Here combinatorial contribution rate is defined as a stopping rule instead of prescribing the number of selected symptoms. The selected symptoms are input to a supervised BP neural network for diagnosing syndrome. Finally, the feasibility of the method is illustrated through a TCM example.
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