Generalized Distance Measures of Neutrosophic Sets for Coronavirus Disease Analysis

Abstract

The world is currently facing an infectious disease caused by the SARS-CoV-2 virus which is known as coronavirus disease 2019. There are a few symptoms that are linked significantly to the disease. The symptoms and disease relationship can be represented by neutrosophic set values. In general, neutrosophic set give remarkable contributions in denoising, clustering, segmentation, and classification in handling data of many real applications including in the medical field. This study aims to analyse the coronavirus disease risks together with viral fever, malaria, typhoid and chikungunya by several generalized distance measures. An analysis is conducted by using the data on the severity level of five symptoms found in eight different patients. The neutrosophic data are analysed to determine the possibility of patients having any one or a combination of several types of diseases, including coronavirus. A comparative study involving four distance measures is conducted. The results show that the distance measures for all patients were less than 0.5, but the distance measures for all patients for coronavirus were higher than other diseases. From this situation, it can be further concluded that there is a possibility that the patients are not suffering from coronavirus.