Comparison of Boosted C5 over ID3 and C4.5 Abdominal Pain Problem

Abstract

Classification can be used as in the form of data analysis that can be used to extract models describing the important data classes. This aim of this paper is to exmaine the performance of decision tree algorithms. Classification is the task to identify the class labels for instance based on a set of features (attributes). This system presents a comparative study of different decision tree algorithms for abdominal pain problem medical data mining. It classifies the different classes, Induction of decision trees (ID3), classification, regression tree (C4.5) and boosted decision tree, C5 are used for the comparative study. According to the experimental results, boosted C5 has better accuracy over ID3 and C4.5 algorithm.