Study And Application Of Fuzzy Clustering In Intelligence Medical Diagnosis System

Automated medical diagnosis is the direction of development of modern medical diagnosis. The thesis briefly introduces the study and application of intelligence medical diagnosis system, and presents an intelligence medical diagnosis system based on traditional Chinese medicine theory. This diagnosis system is composed of an embedded data acquisition equipment and a PC intelligence diagnosis platform. The data acquisition equipment acquires the human impedance signal;the PC diagnosis platforms is used to set up the impedance database, preprocess the human impedance data, extract some feature parameters, and then classify samples with fuzzy clustering methods. The results of the analysis of hospital stomach trouble related illness cases show there are relations between human impedance feature parameters and different illness cases. Fuzzy clustering can be used to classify the illness cases in diagnosis system, and given the support to the reasoning rules of expert system.The thesis respectively uses Transitive Closure Clustering base on Fuzzy Equivalent Relation, Fuzzy C-Means Clustering base on Objective Function, Subtractive Clustering and FCM Synthesized Clustering Algorithm to analyses the sample data of the diagnosis system, and judges the clustering results on the basis of the results of expert diagnosis. According to the performance and clustering results of three algorithms comparison, Subtractive Clustering and FCM Synthesized Clustering Algorithm is better than two other algorithms. It has better clustering performance and classification results. So it is worth further study when carrying out the research and development of the intelligence medical diagnosis system based on human impedance.