A Neuro-Fuzzy Case Based Reasoning Framework for Detecting Lassa Fever Based on Observed Symptoms

Abstract

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