An Adaptive Neuro Fuzzy Inference System for the Diagnosis of Malaria

Abstract

Malaria is one of African most silent killer diseases. It is caused by different plasmodium species and the most deadly of them is the plasmodium falciprum. Early detection is one of the keys for diagnosing malaria using the symptoms. In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) was developed for the diagnosis of malaria. The system was designed to use the triangular membership function and implements back propagation technique and least square mean as its learning algorithm. It uses the tagaki sugeno fuzzy inference model in providing the rules base of the system. The outcome of the system gave an accuracy of 98% in the classification of malaria patients.