title: Searching for risk factors using multilayer neural network as a classifier.
abstract: A method to determine risk factors for particular outcomes using trained multilayer neural networks is proposed. The basic idea is to measure the partial differentials of the output with respect to input variables of the network. Differentiable activation functions and continuity of input variables is assumed.
authors.affiliation: Department of Statistics, Chinese Academy of Preventive Medicine, Beijing, China.
authors.forename: H
authors.initials: H
authors.lastname: Xue
authors.forename: N
authors.initials: N
authors.lastname: Tatsumi
authors.forename: K
authors.initials: K
authors.lastname: Park
authors.forename: M
authors.initials: M
authors.lastname: Shimizu
authors.forename: T
authors.initials: T
authors.lastname: Kyojima
authors.forename: Y
authors.initials: Y
authors.lastname: Sumiya
authors.forename: S
authors.initials: S
authors.lastname: Kawabata
authors.forename: N
authors.initials: N
authors.lastname: Maeda
authors.forename: D
authors.initials: D
authors.lastname: Sakano
mesh_terms: D000465:Algorithms; D000748:Anemia, Macrocytic / Q000145:classification; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008432:Mathematical Computing; D016571:Neural Networks, Computer; D011897:Random Allocation; D012307:Risk Factors; D016896:Treatment Outcome