Medical Diagnosis via Distance-based Similarity Measure for Rough Neutrosophic Set

Abstract

A rough neutrosophic set theory is a generalization of uncertainty set theory with a
combination of upper and lower approximation and a pair of neutrosophic sets which are
characterized by truth membership degree (T), indeterminacy membership degree (I), and falsity
membership degree (F). This set theory is suitable for representing each criterion’s relation in
medical diagnoses, such as the relation of disease and symptom. This paper aims to propose a model
of medical diagnosis via a distance-based similarity measure of a rough neutrosophic set. The first
phase for the development model involves the roughness measure between information collected
and a lower and upper approximation of rough neutrosophic set theory. Then, it is simultaneously
used with extended Hausdorff distance measure to get the proper medical diagnosis. The result
shows that each patient has a chest problem that contradicts the prior diagnosis. The finding shows
that the roughness approximation is important to get the best result in a close distance-based
similarity measure, especially for uncertainty information.