A Fuzzy Logic Expert System for Diagnosing EHF and Evaluating its Risk Level

Annie Oghenerukevbe Egwali, Francis Aien-Akho Imouokhome


Abstract – The complex nature of Ebola hemorrhagic fever (EHF), particularly at the early stages of its outbreak, makes it difficult to diagnose because it manifests symptoms that are similar to those commonly associated with other diseases. Diagnostic decisions of physicians are therefore subjective, calling for an intelligent system for the diagnosis of EHF. This study proposes a fuzzy logic system that takes account of human limitations of medical professionals in the diagnosis of EHF and evaluation of its Risk Level in patients. To achieve the aim of the study, a fuzzy logic- controlled system was designed and implemented using MATLAB fuzzy logic tool box. Input variables to the fuzzy logic controller are (i) patient’s symptoms (PS), (ii) laboratory test results (TR), (iii) patient’s itinerary history (PH), and (iv) number of days since initial infection (ND). The proposed system, which is able to diagnose EHF and evaluate its risk level in patients, shows varying output values as the input parameters vary; with TR having a dominant effect on the results. Fuzzy Logic is proved here to be a powerful tool to design computer-based health diagnostics systems to deal with vagueness or imprecision in disease diagnosis. The fuzzy logic system designed in this study addresses the limitations that are usually associated with the subjective decisions of physicians in the orthodox procedures of diagnosing EHF and evaluating its risk levels in patients.

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