Research on Intelligent Diagnosing Model Based Similarity Distance
In order to help human expert solve the problem of disease diagnosing, we analyze the comparability and relativity between pattern similarity distance and diagnosis as to the solution means, and pioneer the theoretical model of similarity-distance on the basis of certainty factors vectors and fuzzy membership factors vectors, and its corresponding data structure mode. Furthermore, the software hierarchy of model and recognition algorithm are designed. Experimentation statistics demonstrate that compared with the human expert, the novel model could obtain a satisfying accuracy rate of diagnosis over 85%, and reduce a rate of misdiagnosis effectively.
W. X. Tan and X. P. Wang, "Research on Intelligent Diagnosing Model Based Similarity Distance", Advanced Materials Research, Vols. 308-310, pp. 432-435, 2011