Abstract
In this paper, we propose a new graphical framework for extracting the relevant dietary, social and environmental risk factors that are associated with an increased risk of Nasopharyngeal Carcinoma (NPC) based on a case-control epidemiologic study. This framework builds on the use of Bayesian network for representing statistical dependencies between the random variables. BN is directed acyclic graphs that models the joint multivariate probability distribution underlying the data. These graphical models are highly attractive for their ability to describe complex probabilistic interactions between variables. The graph provides a statistical profile of the recruited population and meanwhile help identify a subset of features that are most relevant for probabilistic classification of the NPC. We report experiment results with the NPC case-study data using a novel constraint-based BN structure learning algorithm. We show how the DAG provides an improved comprehension of NPC etiology. Our findings are compared with the risk factors that were suggested in the recent literature in cancerology.
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Aussem, A., Rodrigues de Morais, S., Corbex, M., Favrel, J. (2009). Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods. In: Torsello, A., Escolano, F., Brun, L. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2009. Lecture Notes in Computer Science, vol 5534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02124-4_6
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DOI: https://doi.org/10.1007/978-3-642-02124-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02123-7
Online ISBN: 978-3-642-02124-4
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