Pattern recognition study of biochemical assays for liver function

Abstract

To extract discriminant information from analytical data, results from eight conventional biochemical tests of liver function and from determinations of two serum bile acids are studied by supervised pattern recognition methods. The population comprised healthy subjects and seven groups of people affected by different liver diseases. The principal components, linear discriminant, k nearest neighbours and Bayesian methods were applied. Because the prediction ability computed on the whole data set was poor, the problem was simplified by dividing the data set into three subsets, each comprising two liver diseases which were contiguous and overlapped in the hyperspace of variables. The prediction ability of the Bayesian method reached 96% at best, 75% at minimum, in the three subsets. Best performance was achieved in distinguishing between healthy subjects and those with mild liver diseases on the basis of four biochemical assays.