A Comparative Analysis of Decision Tree Methods to Predict Kidney Transplant Survival
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Abstract
The decision tree is one of the recent developments of sophisticated techniques for exploring high dimensional databases. In data mining, a decision tree is a predictive model which can be used to represent both classification and regression. The aim of this study is to classify kidney transplant patient’s response based on the set of predictor variables using ensemble methods. This paper also compares the performance of decision tree algorithms (ID3, C4.5 and CART), and ensemble methods such as Random forest, Boosting and Bagging with C4.5 and CART as a base classifier. The result shows that CART with Boosting shows the better result than other methods.
Keywords: CART; C4.5; ID3; Boosting; Bagging; Random forest.
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