BAYESIAN CLASSIFICATION OF VASCULITIS - A SIMULATION STUDY

New classification criteria for vasculitic disorders have recently bee n proposed by the American College of Rheumatology. These classificati on criteria have limitations inherent to the method employed in their development, We propose a different approach to the quantitative analy sis of the manifestations of vasculitis, which may improve the precisi on of classification criteria in this domain. Bayesian classifiers wer e developed for six vasculitides using literature-derived quantitative descriptions of these syndromes. These clinical data were also used i n computer programs designed to generate simulations of vasculitis and control cases. The performance of Bayesian classifiers of vasculitis was then compared to that of the American College of Rheumatology crit eria, using series of computer-simulated vasculitis cases. Bayesian cl assifiers identified simulated vasculitis cases with greater accuracy than those of the corresponding American College of Rheumatology 1990 vasculitis criteria in all six diseases studied. As predicted by theor etical considerations, Bayesian classifiers have the potential to iden tify vasculitis cases more accurately than the proposed American Colle ge of Rheumatology 1990 criteria.