Using Reported Symptoms, a Neuro-Fuzzy Case-Based Reasoning System Can Detect Lassa Fever

Lassa fever is an acute viral haemorrhagic fever that is awfully infectious through infected rodents in themastomysnatalensis species that are complex reservoirs capable of excreting the virus through their urine, saliva, excreta and otherbody fluids to man. The virus is a single stranded RNA virus belonging to the arenaviridae family. It presents no definite signs orsymptoms and clinical analysis is often problematic especially at the early onset of the disease. Accurate diagnosis requires highlyspecialized laboratories, which are expensive and not readily available to the entire populace. Early diagnosis and treatment of Lassafever is very vital for survival. In this study, we identified that fuzzy logic and rule-based techniques are the only artificial intelligencesupported approach that has been used to develop an expert system for diagnosing the dreaded Lassa fever as an alternative to laboratorymethodology. It is noted that rule-based is not an efficient technique in the designing expert systems based on its shortcomings such asopaque relations between rules, ineffective search strategy, and its inability to learn; while the fuzzy based technique does not alsosupport the ability to learn but good in areas such as knowledge representation, uncertainty tolerance, imprecision tolerance, andexplanation ability. Based on these information gathered, the authors decided to design a hybridized intelligent framework driven bythe integration of Neural Network (NN), Fuzzy logic (FL) and Case Based Reasoning (CBR) based on their individual strengths puttogether in order to proffer a quick and reliable diagnosis for Lassa fever infection using observed clinical symptoms that could aidmedical practitioners in decision making.