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

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