A bayesian network for probabilistic reasoning and imputation of missing risk factors in type 2 diabetes

Abstract

We propose a novel Bayesian network tool to model the probabilistic relations between a set of type 2 diabetes risk factors. The tool can be used for probabilistic reasoning and for imputation of missing values among risk factors. The Bayesian network is learnt from a joint training set of three European population studies. Tested on an independent patient set, the network is shown to be competitive with both a standard imputation tool and a widely used risk score for type 2 diabetes, providing in addition a richer description of the interdependencies between diabetes risk factors.