A Fuzzy Expert System for the Management of Malaria

Abstract

Malaria represents major public health problems in the tropics. The harmful effects of malaria parasites to the human body cannot be underestimated. In this paper, a fuzzy expert system for the management of malaria (FESMM) was presented for providing decision support platform to malaria researchers, physicians and other healthcare practitioners in malaria endemic regions. The developed FESMM composed of four components which include the Knowledge base, the Fuzzification, the Inference engine and Defuzzification components. The fuzzy inference method employed in this research is the Root Sum Square (RSS). The Root Sum Square of drawing inference was employed to infer the data from the fuzzy rules developed. Triangular membership function was used to show the degree of participation of each input parameter and the defuzzification technique employed in this research is the Centre of Gravity (CoG). The fuzzy expert system was designed based on clinical observations, medical diagnosis and the expert’s knowledge. We selected 35 patients with malaria and computed the results that were in the range of predefined limit by the domain experts.