An improved medical diagnosing of acute abdominal pain with decision tree

In medical decision making (e.g., classification) we expect that decision will be made effectively and reliably. Decision making systems with their ability to learn automatically seem to be very appropriate for performing such tasks. Decision trees provide high classification accuracy with simple representation of gathered knowledge. Those advantages cause that decision trees have been widely used in different areas of medical decision making. In this paper we present characteristic of univariate and multivariate decision tree. We apply those classifiers to the problem of acute abdominal pain diagnosis.