PCA for fuzzy data and similarity classifier in building recognition system for post-operative patient data

In this article we propose a method which tackles a problem where data is linguistic instead of real valued numbers. The proposed method starts with representing data as fuzzy numbers. Then generalized principle component analysis (PCA) is used, which can be used to reduce the data dimensionality and also to clear out some irregularitises from the data. After this, the data is defuzzified and then the similarity classifier is used to get the required classification accuracy. Here post-operative patient data set is used to build this expert system to determine based on hypothermia condition, whether patients in a post-operative recovery area should be sent to Intensive Care Unit, general hospital floor or go home. What makes this task particularly difficult is that most of the measured attributes have linguistic values (e.g. stable, moderately stable, unstable, etc.). Results are compared to existing result in literature and this system provides mean classification accuracy of 62.7% where as second highest reported results are with linguistic hard C-mean with 53.3%.