Some distance based similarity measure of Neutrosophic sets and the application to medical diagnosis / Nurul Najiha Fakhrarazi, Nurnisa Nasuha Mohd Yusof and Nik Nur Aisyah Nik Hassan

Diagnosis is the process of determining which illness or condition is causing a person’s symptoms and indications. The patient’s medical history and physical examination are frequently used to acquire the essential informations for diagnosis. Medical diagnosis have many uncertain informations. The neutrosophic set is a combination of the fuzzy set and the intuitionistic fluffy set which can deal with uncertainty, vagueness and imprecision. Thus, this study aims to focus on distance based similarity measure of neutrosophic set to analyse medical diagnosis patient’s risk. In this study, some distance based similarity measures will be based on Hausdorff distance, Hamming distance, and Euclidean distance. Then, a case study is conducted by using the data on the severity level of the existed symptoms and diagnosis found in one patient. The three distance-based similarity measures resulting the values more than 0.5 which show the patient possibly suffer from the disease. The obtained similarity measures are then ranking to identify patient disease. After that by using entropy weight method to make another comparison between three distance-based similarity measures which show more consistent result. This evaluation and diagnosis approach is applicable to a wide variety of other resources and the environmental problems.