Distance-based similarity measure for single-valued Neutrosophic sets and its application in medical diagnosis / Mohamad Amirul Fahmi Yusaffandy and Wan Abdullah Azim Wan Nor Azmi

Neutrosophic set is the extension of the fuzzy set which cannot represent uncertainty data. Neutrosophic set can relate it as being able to characterize the attributes in membership-values of truth, falsity and indeterminacy. Many real life problems involves uncertainty and inconsistent information. One of them is the medical diagnosis which contains a lot of attributes that is inconsistent, uncertain and imprecise. As this information is very vital to the doctor to make a decision-making such as early diagnosis, hence, this study aims to formulate distance based measure of neutrosophic set in order to solve decision making problem related with medical diagnosis. In this project, distance-based similarity measure has been formulate from the existing measures which are Euclidean distance and cosine similarity measures. The distance based similarity measure is applied into two data. The first data are consist of four patients with five symptoms and five diseases while the second data are consist of one woman with eight symptoms and six diagnoses. Each patients then diagnose with the disease based on the similarity measures. The highest value of similarity measure show that the patient is suffering with that recognized disease.