Application of pattern recognition techniques to discrete clinical data

Two pattern recognition techniques capable of handling unordered discrete data are applied to the analysis and classification of clinical data. The first technique uses a dependence-tree approach for classifying and datecting patterns from the discrete data. The second technique is based on modulo-2 linear transformation and approximation of probability distributions. Both techniques are applied to clinical data of two categories of liver diseases: acute viral hepatitis and chronical active hepatitis. The data selected by a physician for identifying and discriminating these two liver diseases consists of 12 features, each feature having a range of two or three discrete values. Experimental results using the two techniques are presented.