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Mathematical Problems in Engineering
Volume 2014, Article ID 524621, 10 pages
Research Article

Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network

Liver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China

Received 2 January 2014; Revised 20 March 2014; Accepted 21 March 2014; Published 22 May 2014

Academic Editor: Gongnan Xie

Copyright © 2014 Naiping Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Alcoholic liver diseases cause high incidence of death worldwide. However, computational diagnosis and classification of alcoholic hepatitis have not yet been established. In this study, we used general regression neural network (GRNN) model with a high-performance classification ability to diagnose and classify alcohol hepatitis. We used tenfold cross-validation to demonstrate the error rate of networks. The results show an accuracy of 80.91% of the back diagnosis in 110 patients and the accuracy of 81.82% of predicting-diagnosis in 11 patients referring to the clinical diagnosis made by a group of experts. This study suggested that using the liver function tests as the input layer variables of GRNN model could accurately diagnose and classify alcoholic liver diseases.