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.

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.