A belief rule based expert system to diagnose dengue fever under uncertainty

Dengue Fever is a debilitating mosquito-borne disease, causing sudden fever, leading to fatality in many cases. A Dengue patient is diagnosed by the physicians by looking at the various signs, symptoms and risk factors of this disease. However, these signs, symptoms and the risk factors cannot be measured with 100% certainty since various types of uncertainties such as imprecision, vagueness, ambiguity, and ignorance are associated with them. Hence, it is difficult for the physicians to diagnose the dengue patient accurately since they don't consider the uncertainties as mentioned. Therefore, this paper presents the design, development and applications of an expert system by incorporating belief rule base as the knowledge representation schema as well as the evidential reasoning as the inference mechanism with the capability of handling various types of uncertainties to diagnose dengue fever. The results generated from the expert system are more reliable than from fuzzy rule based system or from human expert.