Selection of classifier in acute abdominal pain diagnosis with decision tree model

The article presents the application of the decision tree classifier to the acute abdominal pain diagnosis. The recognition task model is based on a decision tree. In this model the decision tree structure is given by the experts. For the assumed structure of the decision tree the locally optimal strategy is considered. The problem discussed in the work shows a selection of different classifiers (parameters) to the internal nodes of the decision tree. Experiments conducted for selected medical diagnosis problem shows that the use of different parameters for k-NN classification can improve the quality of classification in comparison with the situation if it is used with the same parameter for all internal nodes of the decision tree.