Kuang-Yi Chou3, Huan-Chao Keh1, Nan-Ching Huang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Shing-Hwa Lu2, Ding-An Chiang1 and Yuan-Cheng Cheng1

1Department of CSIE, Tamkang University, Tamsui, Taiwan 251, R.O.C.
2Department of Urology, School of Medicine, National Yang-Ming University and Department of Urology, Zhong Xiao Branch, Taipei City Hospital, Urological Center, Taipei, Taiwan 115, R.O.C.
3Center of General Education, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan 112, R.O.C.


Received: December 2, 2010
Accepted: June 20, 2011
Publication Date: March 1, 2012

Download Citation: ||https://doi.org/10.6180/jase.2012.15.1.10  


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.

Keywords: Decision Tree, Classification, Irrelevant Values, Missing Branches, Medical Examination


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