Data mining based Bayesian networks for best classification

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)).