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

Abstract EN

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.