title: Structured neural network of medical diagnosis for hepatobiliary diseases.
abstract: 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.
authors.affiliation: Department of Public Health, School of Medicine, St. Marianna University, Kawasaki, Kanagawa 216, Japan.
authors.forename: K
authors.initials: K
authors.lastname: Yoshida
authors.forename: T
authors.initials: T
authors.lastname: Izuno
authors.forename: E
authors.initials: E
authors.lastname: Takahashi
authors.forename: H
authors.initials: H
authors.lastname: Sugimori
authors.forename: M
authors.initials: M
authors.lastname: Miyakawa
mesh_terms: 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