Diagnosis of Hypothyroid Disease by Using Decision Tree Induction

Abstract

Data mining is the process of discovering useful information underlying the data. Powerful techniques are needed to extract patterns from large data because traditional statistical tools are efficient enough any more. Classification and prediction are two forms of data analysis that can be used predict future data trends. In this paper, we proposed a diagnosis system for Hypothyroid disease by using decision tree induction algorithm. Decision tree induction algorithms have been used for classification in a wide range of application domains. Decision trees are potentially powerful predictors and explicitly represent the structure of a dataset. In this system, C4.5 classification algorithm is analyzed to build the model from a given set of training data and then produced classification rules. It examines the patient is positive or negative in hypothyroid. The performance evaluation of our system is also discussed in this paper.