Optimizing fuzzy clinical decision support rules using genetic algorithms.
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%
Krajnak
Michael
M
GE Healthcare Information Technology, Milwaukee, WI 53226. USA.
Xue
Joel
J
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D017143:Fuzzy Logic; D006801:Humans; D008957:Models, Genetic; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012680:Sensitivity and Specificity; D013269:Stochastic Processes