Application of pattern recognition techniques to discrete clinical data
Andrew C. Wong, Tzay Y. Young, Philip Liu · 1976
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.