Decision Support in Medical Data Using 3D Decision Tree Visualisation

Abstract

Decision making is a difficult task, especially in the medical domain. Decision trees are among the most often used supervised machine learning algorithms, primarily due to their straightforward interpretation through rules, they are composed of. Visualizations of decision trees can help with the understanding of data and decision rules on top of them. This paper proposes a 3D decision tree visualization method that allows effective decision tree exploration and help with decision tree interpretation. The main contribution of this visualization is the ability to display global tree structure and the difference in the distributions of values between subsets created by individual decision nodes. The approach is evaluated using a design study on a typical medical dataset and on a synthetic dataset with controlled properties. It shows the visualization’s ability to display individual components of the decision rule and the ability to see other correlated factors in the decision and the change it causes in subsets of data created by individual decision nodes.