Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems

One of the hardest technical tasks in employing Bayesian network models in practice is obtaining their numerical parameters. In the light of this difficulty, a pressing question, one that has immediate implications on the knowledge engineering effort, is whether precision of these parameters is important. In this paper, we address experimentally the question whether medical diagnostic systems based on Bayesian networks are sensitive to precision of their parameters.

The test networks include Hepar II, a sizeable Bayesian network model for diagnosis of liver disorders and six other medical diagnostic networks constructed from medical data sets available through the Irvine Machine Learning Repository. Assuming that the original model parameters are perfectly accurate, we lower systematically their precision by rounding them to progressively courser scales and check the impact of this rounding on the models' accuracy.

Our main result, consistent across all tested networks, is that imprecision in numerical parameters has minimal impact on the diagnostic accuracy of models, as long as we avoid zeroes among parameters.

The experiments' results provide evidence that as long as we avoid zeroes among model parameters, diagnostic accuracy of Bayesian network models does not suffer from decreased precision of their parameters.