Development of a Fuzzy Logic Predictive Model for Lassa Fever Risk Detection

Abstract- Although there is no vaccine to prevent Lassa fever, symptomatic therapy increases the patient's chances of survival. The antiviral medicine Ribavirin demonstrated being effective when administered early enough in the illness. Lassa fever clinical research is difficult. To lower the mortality and morbidity of Lassa fever, urgent research is underway. Through a search of pertinent literature and organized interviews with medical professionals, risk factors for Lassa fever were discovered. Fuzzy Logic Toolbox, MATLAB® R2009a, was used to create and simulate the model for predicting Lassa fever risk. The risk factors and target risk were created using triangle membership functions, which fuzzy inference engine inferred 384 rules from six risk parameters. The target class has No, Low, Moderate, and High risk as the linguistic labels. In the MATLAB environment, the validity of the inferred rules was tested. This work built and developed a model for predicting Lassa fever risk, which patient and non-medical specialists can use for early Lassa fever risk diagnosis. This will help decrease the mortality rate because early treatment aids in recovery. Keywords: Lassa fever, Rodent, Fuzzy Logic, Predictive Model, Simulation, Risk Factor.