Comparison of Bayesian network and decision tree methods for predicting access to the renal transplant waiting list
Abstract
The study compares the effectiveness of Bayesian networks versus Decision Trees for predicting access to renal transplant waiting list in a French healthcare network. The data set consisted in 809 patients starting renal replacement therapy. The data were randomly divided into a training set (90%) and a validation set (10%). Bayesian network and CART decision tree were built on the training set. Their predictive performances were compared on the validation set. The age variable was found to be the most important factor in both models. Both models were highly sensitive and specific: sensitivity 90.0% (95%CI: 76.8-100), specificity 96.7% (95%CI: 92.2-100). Moreover, the models were complementary since the Bayesian network provided a global view of the variables' associations while the decision tree was more easily interpretable by physicians. These approaches provide insights on the current care process. This knowledge could be used for optimizing the healthcare process.