Analysis using Adaptive Tree Structured Clustering Method for Medical Data of Patients with Coronary Heart Disease
11-02-SC080003.pdf 311 KB
Mackin, Kenneth J.
It is known that the classification of medical data is difficult problem because the medical data has ambiguous information or missing data. As a result, the classification method that can handle ambiguous information or missing data is necessity. In this paper we proposed an adaptive tree structure clustering method in order to clarify clustering result of selforganizing feature maps. For the evaluating effectiveness of proposed clustering method for the data set with ambiguous information, we applied an adaptive tree structured clustering method for classification of coronary heart disease database. Through the computer simulation we showed that the proposed clustering method was effective for the ambiguous data set.
Fourth International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2008
|date of issued||
IEEE SMC Hiroshima Chapter
(c) Copyright by IEEE SMC Hiroshima Chapter.
Graduate School of Engineering