Bayes Theorem and Conditional Dependence of Symptoms: Different Models Applied to Data of Upper Gastrointestinal Bleeding

Abstract

Summary: In computer-aided prognosis theoretical arguments in favour of a sophisticated model often do not result in a markedly better performance. In this study, straightforward extensions of the simple and comprehensive independent Bayes model, taking interactions between variables into consideration, are investigated, using a clinical data set of upper gastrointestinal bleeding, four sets of variables and different measures of performance (discriminatory ability, sharpness, reliability). For all criteria of performance, the differences between the models were small in the sets with few variables. In the sets with many variables there were marked differences between the models; however, no model was superior in all aspects of performance. Incorporation of interactions in models based upon Bayes’ theorem are worthwhile if many variables are used and the discriminatory ability is considered.