Diagnosis of TB Disease by Using Decision Tree Induction

Abstract

Decision tree learning algorithm has been successfully using in data minng for training data set. In this system, the main task performed is using inductive methods to the given values of attributes of an unkown object to determine appropriate classification according to decision tree rules. Decision tree inductive method used in this system to determine the appropriate TB disease classification according to decision tree rules. This system contained three classses of TB disease such as P(Pulmonary Tuberculosis), EP (Extra Pulmonary Tuberculosis) and no-TB (class for non TB-suffering patients). The classifier classifies TB diseases based on the symptoms of patients. The holdout mehtod and bootstraping used in this system to analyze the accuracy of the classifier.