Logarithmic similarity measure of dynamic neutrosophic cubic sets and its application in medical diagnosis

In the medical diagnosis process, the symptoms of patients are always changing with time and relative to dynamical information at different time intervals. However, existing information expression methods usually neglect the temporal correlation of information expression, and hardly depict certain and uncertain information on complex medical diagnosis problems. For the first time, the study presents dynamic neutrosophic cubic set (DNCS) to express the patient's disease symptoms in a time sequence (different time intervals). Then, the logarithmic similarity measure (LSM) of DNCSs is put forward and their properties are verified. After that, a medical diagnosis method is constructed on the basis of the proposed logarithmic similarity measure in DNCS setting, where the disease symptom information collected from different time intervals is given by the form of DNCSs. Lastly, a medical diagnosis example is used to demonstrate its applicability, and then the diagnosis results indicate that the presented method is effective and feasible. (C) 2019 Elsevier B.V. All rights reserved.