Interval Type II Fuzzy Expert System for Diagnosis Visceral Leishmaniasis

Abstract

Leishmaniasis is neglected tropical a protozoan infectious disease caused by Leishmania parasites. VL (also known as Kala-Azar) is the most severe chronic form of Leishmaniasis, almost always fatal if untreated. The annual burden of VL in Ethiopia is estimated to be between 4,500 and 5,000 cases. In a medical environment, incomplete information and imprecise are driven to making an incorrect medical decision and maximize the rate of morbidity and mortality. Fuzzy logic technology enables to provide a simple way to attain to a certain conclusion from vague, imprecise and ambiguous medical data. Clinical suspicion of VL is complex because of its clinical manifestation are shared by other commonly occurring tropical disease like malaria, typhoid and tuberculosis. Several studies were conducted on VL, nevertheless, no one addresses the VL diagnosis using learning capacity. This thesis, we developed and investigated the application of an intelligent interval Type-2 Fuzzy Logic Expert System for diagnosis Visceral Leishmaniasis (VLDES) to assist health workers easy diagnosis a patient and decision-makers to prevent the expansion epidemic. The Type-2 Fuzzy Logic System would be clear to present a highly interpretable and transparent model that is very suitable for the handling uncertainties in the input factors and converting the accumulated data to linguistic formats. First we acquired well organized domain expert knowledge via interview questions and relevant document from the university of Gondar Kala-Azar Treatment and Research center. Secondly, we defined the parameters of fuzzy membership functions of Type-1 Fuzzy Logic System mentioned based on domain knowledge with intensive discussion. Finally, we employed to define uncertainty of the Footprint of Uncertainty (FOU) percentage of Interval Type-2 Fuzzy Logic System. We obtained  98.97% classification accuracy respectively from fifteen patients testing data. This show that the proposed Interval Type-2 Fuzzy Logic Classification System provides a more interpretable model that Diagnosis VL.