Structured neural network of medical diagnosis for hepatobiliary diseases.
This study aims to apply a structured neural network system for improving diagnostic capabilities of hepatobiliary diseases. Several neural networks were organized according to a priori knowledge. A priori knowledge consists of five pathological states. We compared the diagnostic accuracy of discriminant function, ordinary and structured neural networks. The diagnostic capabilities among testing data were 96% by structured neural network, 70% by ordinary neural network, and 66% by discriminant function. The structured neural network had the significantly higher diagnostic accuracy than the other two methods.
Yoshida
K
K
Department of Public Health, School of Medicine, St. Marianna University, Kawasaki, Kanagawa 216, Japan.
Izuno
T
T
Takahashi
E
E
Sugimori
H
H
Miyakawa
M
M
D000368:Aged; D000465:Algorithms; D001660:Biliary Tract Diseases / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D016014:Linear Models; D008107:Liver Diseases / Q000175:diagnosis*; D008875:Middle Aged; D016571:Neural Networks, Computer