A classification method based on similarity measures of generalized fuzzy numbers in building expert system for postoperative patients.
In this research, we concentrate to build an expert system for a problem where task is to determine where patients in a postoperative recovery area should be sent to next. Data set created from postoperative patients is used to build proposed expert system to determine, based on hypothermia condition, whether patients in a postoperative recovery area should be sent to Intensive Care Unit (ICU), 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.). We are using generalized fuzzy numbers to model the data and introduce new fuzzy similarity based classification procedure which can deal with these linguistic attributes and classify them accordingly. Results are compared to existing result in literature, and this system provides mean classification accuracy of 66.2% which compared well to earlier results reported in literature.
Luukka
Pasi
P
Laboratory of Applied Mathematics, Lappeenranta University of Technology, 20, FIN-53851 Lappeenranta, Finland. pasi.luukka@lut.fi
D001831:Body Temperature; D019295:Computational Biology; D003422:Critical Care; D016208:Databases, Factual; D003657:Decision Making; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D007035:Hypothermia / Q000628:therapy; D010351:Patient Discharge; D011182:Postoperative Care / Q000379:methods* / Q000706:statistics & numerical data; D011183:Postoperative Complications / Q000628:therapy