Optimizing Fuzzy Clinical Decision Support Rules Using Genetic Algorithms

Author

Krajnak, Michael ; Xue, Joel

Author_Institution

GE Healthcare Inf. Technol., Milwaukee, WI

fYear

2006

fDate

Aug. 30 2006-Sept. 3 2006

Firstpage

5173

Lastpage

5176

Abstract

In this paper, we present a technique for optimizing a fuzzy system using a genetic algorithm that works for patient status monitoring in the operating room. The genetic algorithm adjusts rule weights, outputs, and input membership functions to maximize the area under a receiver operator curve (ROC) for final classification. Compared to pre-optimization, the optimized fuzzy inference system increased ROC area from 0.68 to 0.77, which can be translated to an increase in specificity from 74% to 82%, at a fixed sensitivity of 58%

Keywords

decision support systems; fuzzy logic; genetic algorithms; inference mechanisms; medical information systems; patient monitoring; sensitivity analysis; ROC; fuzzy clinical decision support rules; fuzzy inference system; genetic algorithms; membership functions; operating room; patient status monitoring; pre-optimization; receiver operator curve; rule weights; Anesthesia; Anesthetic drugs; Biomedical monitoring; Blood pressure; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Heart rate; Patient monitoring;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Conference_Location

New York, NY

ISSN

1557-170X

Print_ISBN

1-4244-0032-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2006.260366

Filename

4462969