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In this paper, we evaluate the effectiveness of four Bayesian network classifiers as potential tools for the histopathological diagnosis of chronic idiopathic inflammatory bowel disease (CIIBD) using a database containing endoscopic colorectal biopsies. CIIBD is the generic term for referring to two ailments known as Crohn's disease and ulcerative colitis. The results show that the defined histological attributes, considered relevant in the medical literature for the diagnosis of CIIBD, are very good for the distinction between normal samples and CIIBD samples (Crohn's disease and ulcerative colitis combined into a single category) but less good for the explicit distinction between Crohn's disease and ulcerative colitis. The findings suggest an intrinsic impossibility of selecting a set of features for achieving good balance for both sensitivity and specificity for Crohn's disease and ulcerative colitis.

Diagnosis of Chronic Idiopathic Inflammatory Bowel Disease Using Bayesian Networks