JoWUA

Abstract

Chikungunya is a virus-related disease, bring about by the virus called CHIKV that spreads through mosquito biting. This virus first found in Tanzania, while blood from patients was isolated. The common signs and symptoms, associated with Chikungunya are considered as fever, joint swelling, joint pain, muscle pain and headache. The examination of these signs and symptoms by the physician constitutes the typical preliminary diagnosis of this disease. However, the physician is unable to measure them with accuracy. Therefore, the preliminary diagnosis in most of the cases could suffer from inaccuracy, which leads to wrong treatment. Hence, this paper introduces the design and implementation of a belief rule based expert system (BRBES) which is capable to represent uncertain knowledge as well as inference under uncertainty. Here, the knowledge is illustrated by employing belief rule base while deduction is carried out by evidential reasoning. The real patient data of 250 have been considered to demonstrate the accuracy and the robustness of the expert system. A comparison has been performed with the results of BRBES and Fuzzy Logic Based Expert System (FLBES) as well as with the expert judgment. Furthermore, the result of BRBES has been contrasted with various data-driven machine learning approaches, including ANN (Artificial Neural networks) and SVM (Support Vector Machine). The reliability of BRBESs was found better than those of datadriven machine learning approaches. Therefore, the BRBES presented in this paper could enable the physician to conduct the analysis of Chikungunya more accurately.