Constructing naive Bayesian classifiers for veterinary medicine: a case study in the clinical diagnosis of classical swine fever

For diseases of which the clinical diagnosis is uncertain, naive Bayesian classifiers can be of assistance to the veterinary practitioner. These simple probabilistic models have proven to be very powerful for solving classification problems in a variety of domains, but are not yet widely applied within the veterinary domain. In this paper, naive Bayesian classifiers and methods for their construction are reviewed. We demonstrate how to construct full and selective classifiers from a data set and how to build such classifiers from information in the literature. As a case study, naive Bayesian classifiers to discriminate between classical swine fever (CSF)-infected and non-infected pig herds were constructed from data collected during the 1997/1998 CSF epidemic in the Netherlands. The resulting classifiers were studied in terms of their accuracy and compared with the optimally efficient diagnostic rule that was reported earlier by Elbers et al. (2002). The classifiers were found to have accuracies within the range of 67-70% and performed comparable to or even better than the diagnostic rule on the available data. In contrast with the diagnostic rule, the classifiers had the advantage of taking both the presence and the absence of particular clinical signs into account, which resulted in more discriminative power. These results indicate that naive Bayesian classifiers are promising tools for solving diagnostic problems in the veterinary field.