Data mining based Bayesian networks for best classification

Authors:
Abdelaziz Ouali;Amar Ramdane Cherif;Marie-Odile Krebs
Affiliations:
Faculty of Medicine Paris Descartes/ University Paris Descartes/ INSERM E0117, Pathophysiology of Psychiatric Disorders/ 2 ter rue d'Alé/sia 75014 Paris, France and PRISM Laboratory, Universit ...;PRISM Laboratory, University of Versailles/ 45 Av. des Etats-Unis 78035, Versailles, France;Faculty of Medicine Paris Descartes/ University Paris Descartes/ INSERM E0117, Pathophysiology of Psychiatric Disorders/ 2 ter rue d'Alé/sia 75014 Paris, France
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

Schizophrenia is a frequent and devastating disorder beginning in early adulthood. Until now, the heterogeneity of this disease has been a major pitfall for identifying the aetiological, genetic or environmental factors. Age at onset or several other quantitative variables could allow categorizing more homogeneous subgroups of patients, although there is little information on the boundaries for such categories. The Bayesian networks classifier (BNs) approach is one of the most popular formalisms for reasoning under uncertainty. Using a data set including genotypes of selected candidate genes for schizophrenia, BNs were used to determine the best cut-off point for three continuous variables (i.e. age at onset of schizophrenia (AFC & AFE) and neurological soft signs (NSS)).