APPLICATION OF NEURAL NETWORK TO THE ASSESSMENT FOR THE DEGREE OF HEPATIC FIBROSIS

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The purpose of this study is to investigate the ability of neural network to discriminate the two subtypes, chronic active hepatitis 2A and 2B,on the basis of the patterns of the five blood biochemical parameters. Serum levels of cholinesterase, alkaline phosphatase, type IV collagen, and hyaluronate were used as variables. The neural network trained with the data from 31 patients with chronic active hepatitis 2A or 2B.The ability of the network to predict the diagnosis of the patients who were additionally recruited was tested with a separate group. A neural network with a 5 input neurons, 10 hidden neurons, and 2 output neurons correctly classified all 31 patients. This network correctly predicted the diagnosis for 78% of the additional group. These results suggested that neural network are useful for the differentiation of two forms of chronic active hepatitis by a less invasive blood biochemical analysis.