The Study of Misclassification Probability in Discriminant Model of Pattern Identification for Stroke

Background. Pattern identification (PI) is the basic system for diagnosis of patients in traditional Korean medicine (TKM). The purpose of this study was to identify misclassification objects in discriminant model of PI for improving the classification accuracy of PI for stroke. Methods. The study included 3306 patients with stroke who were admitted to 15 TKM hospitals from June 2006 to December 2012. We derive the four kinds of measure (D, R, S, and C score) based on the pattern of the profile graphs according to classification types. The proposed measures are applied to the data to evaluate how well those detect misclassification objects. Results. In 10-20% of the filtered data, misclassification rate of C score was highest compared to those rates of other scores (42.60%, 41.15%, resp.). In 30% of the filtered data, misclassification rate of R score was highest compared to those rates of other scores (40.32%). And, in 40-90% of the filtered data, misclassification rate of D score was highest compared to those rates of other scores. Additionally, we can derive the same result of C score from multiple regression model with two independent variables. Conclusions. The results of this study should assist the development of diagnostic standards in TKM.