While Bayesian network models may contain a handful of numerical parameters that are important for their quality, several empirical studies have confirmed that overall precision of their probabilities is not crucial. In this paper, we study the impact of the structure of a Bayesian network on the precision of medical diagnostic systems. We show that also the structure is not that important - diagnostic accuracy of several medical diagnostic models changes minimally when we subject their structures to such transformations as arc removal and arc reversal. © 2014 Springer International Publishing.
CITATION STYLE
Oniśko, A., & Druzdzel, M. J. (2014). Impact of Bayesian network model structure on the accuracy of medical diagnostic systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8468 LNAI, pp. 167–178). Springer Verlag. https://doi.org/10.1007/978-3-319-07176-3_15
Mendeley helps you to discover research relevant for your work.