Comparison between statistical and fuzzy approaches for improving diagnostic decision making in patients with chronic nasal symptoms
This paper compares a fuzzy model, expressed in rule-form, with a well known statistical approach (i.e. logistic regression model) for diagnostic decision making in patients with chronic nasal symptoms. The analyses were carried out using a database obtained from a questionnaire administered to 1359 patients with nasal symptoms containing personal data, clinical data and skin prick test (SPT) results. Both the fuzzy model and the logistic regression model developed were validated using a data set obtained from another medical institution. The accuracy of the two models in identifying patients with positive or negative SPT was similar. This study is a preliminary step to the creation of a software that primary care doctors can use to make a diagnostic decision, when deciding whether patients with nasal symptoms need allergy testing or not.
diagnostic decision making nasal symptoms fuzzy model logistic regression model skin prick test fuzzy relations approximate reasoning fuzzy inference systems