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.
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Sambo, F. et al. (2015). A Bayesian Network for Probabilistic Reasoning and Imputation of Missing Risk Factors in Type 2 Diabetes. In: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_22
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DOI: https://doi.org/10.1007/978-3-319-19551-3_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19550-6
Online ISBN: 978-3-319-19551-3
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