Treatment Response Classification in Randomized Clinical Trials: A Decision Tree Approach

Abstract

Decision Trees are a subfield of machine learning technique within the larger field of artificial intelligence. It is a supervised learning technique for classification and prediction. The decision trees are widely used for outcome prediction under various treatments for disease cure, prevention, toxicity and relapse. The aim of the paper is to compare the decision tree algorithms in classifying tuberculosis patient’s response under randomized clinical trial condition. Classification of patient’s responses to treatment is based on bacteriological and radiological methods. Three decision tree approaches namely C4.5, Classification and regression trees (CART), and Iterative dichotomizer 3 (ID3) methods were used for the classification of response. The result shows that C4.5 decision tree algorithm performs better than CART and ID3 methods. Keywords: Decision tree. Data mining.CART.C4.5. ID3.