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Proceedings Paper

Diagnosis of hepatitis by use of neural network learning
Author(s): Hong-Qing Fan; Qy-zi Zhang
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Paper Abstract

An attempt is made to find a new way for better diagnosis of hepatisis through application of artificial neural network theory. Learning from a given sample set, the neural network is used to establish a nonlinear mapping between various factors, such as symptoms, signs, and laboratorial experiments, and diagnosis of hepatisis. It is proved that the used network and values of weight after learning are available to the identification of equivalent class of a new pattern of hepatisis. In this paper, the knowledge learning and learning algorithms used in diagnosis are mainly discussed, an optimal generalization algorithm based on the error decrease algorithm and used to train multilayer feedforward is presented; meanwhile, the application results and their effectiveness are introduced.

Paper Details

Date Published: 2 March 1994
PDF: 9 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.170002
Show Author Affiliations
Hong-Qing Fan, The First Epidemic Hospital (China)
Qy-zi Zhang, Northeastern Univ. (China)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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