Comparison of Bayesian Network and Decision Tree Methods for Predicting Access to the Renal Transplant Waiting List Sahar BAYAT a, 1 , Marc CUGGIA a , Delphine ROSSILLE a , Michèle KESSLER b , Luc FRIMAT c a INSERM U936, Université Rennes 1, IFR 140, Rennes, France b Service de Néphrologie, Hôpitaux du Brabois, Nancy, France c EA 4003, Nancy Université, France 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. Keywords. decision support, decision tree, Bayesian network, healthcare network, renal transplant waiting list 1. Introduction Incidence and prevalence of End-Stage Renal Disease (ESRD) requiring Renal Replacement Therapy (RRT) (hemodialysis, peritoneal dialysis or kidney transplantation) continue to increase. Kidney transplantation is associated with longer survival and lower long-term cost. But, given the graft shortage, transplantation is not commonly achievable. On the other hand, the selection criteria of the potential transplant recipient diverge from one centre to another. Ideally, placement on the waiting list should be solely based on medical factors. Previous studies showed that female gender, old age, distance from transplantation centre and private ownership of dialysis facilities have been associated with poor access to kidney transplant waiting lists [1, 2]. Previously [3, 4] we showed that access to the renal transplant waiting list in NEPHROLOR, a French healthcare network, is primarily associated with age and medical factors. The factors were identified by conventional statistical methods and 1 Corresponding Author: Sahar Bayat, Département d’Information Médicale (DIM), Hôpital Pontchaillou, 2 rue Henri Le Guilloux, 35033 Rennes, France; E-mail: sahar.bayat@chu-rennes.fr. Medical Informatics in a United and Healthy Europe K.-P. Adlassnig et al. (Eds.) IOS Press, 2009 © 2009 European Federation for Medical Informatics. All rights reserved. doi:10.3233/978-1-60750-044-5-600 600