NAIVE BAYESIAN CLASSIFIERS FOR THE CLINICAL DIAGNOSIS OF CLASSICAL SWINE FEVER

摘要

Naive Bayesian classifiers have been applied successfully for solving diagnostic problems in the medical domain, but are relatively new to the veterinary field. To demonstrate their potential, naive Bayesian classifiers were constructed for discriminating between Classical Swine Fever (CSF) infected and uninfected herds using data on 490 herds, collected during the 1997/1998 CSF epidemic in the Netherlands. A full naive Bayesian classifier and a selective one were constructed, and their classification accuracies were compared to that of a previously published diagnostic rule. The full classifier had a higher accuracy than the diagnostic rule, and the selective classifier proved to be comparable to the rule. In contrast with the diagnostic rule, thetwo classifiers had the advantage of taking both the presence and the absence of clinical signs into account, which resulted in more discriminative power.