The irrelevant values problem of decision tree in medical examination

Data mining technique is extensively used in medical application. One of key tools is the decision tree. When a decision tree is represented by a collection of rules, the antecedents of individual rules may contain irrelevant values problem. When we use this complete set of rules to medical examinations, the irrelevant values problem may cause unnecessary economic burden both to the patient and the society. We used a hypothyroid disease as an example for the study of irrelevant values problem of decision tree in medical examination. Hypothyroid disease is used to associate to the mechanism of thyroid-stimulating hormone (TSH). Physicians will combine lots of information; such as patient's clinical records, medical images, and symptoms, prior to the final diagnosis and treatment, especially surgical operation. Therefore, to avoid generating rules with irrelevant values problem, we propose a new algorithm to remove irrelevant values problem of rules in the process of converting the decision tree to rules utilizing information already present in the decision tree. Our algorithm is able to handle both discrete and continuous values.