On Classification of Linguistic Data-Case Study: Post-operative

This article presents simple yet efficient way to classify Post-operative patient data. The classification task of this database is to determine where patients in a postoperative recovery area should be sent to next. Because hypothermia is a significant concern after surgery, the attributes correspond roughly to the body temperature measurements. What makes classification task difficult here is that the most of the attributes are given by linguistic values. Method proposed in this article starts by representing linguistic variables by suitable numbers, which are later normalized into the values between 0 and 1. Next phase this data is classified using simple similarity classifier. Results are compared to the existing results and method presented in this paper provides mean accuracy of 65.23% whereas second highest reported result is 62.67% using similarity classifier with PCA and membership functions.