Classification of Thyroid Disease by Using Data Mining Models: A Comparison of Decision Tree Algorithms

The main objective of this study is to provide a compact source of reference for the researchers who want to use decision tree which is an important tool of data mining technology in their area of work. With this aim in mind, we compared widely used decision tree algorithms to classify types of thyroid disease and compared their performances according to six performance metrics (ACC(%), MAE, PRE, REC, FME, and Kappa Statistic). We hope that this study can provide a useful overview of the current work of this field and highlight how to apply decision tree algorithms as a tool of data mining technology.