DESIGN AND IMPLEMENTATION OF A NEUROFUZZY LOGIC SYSTEM FOR DIAGNOSIS OF MALARIA

ABSTRACT This project presents work in Neuro Fuzzy systems that enhances the decision making processes in medical diagnosis of malaria. Malaria is a serious, life-threatening blood disease caused by parasites transmitted to humans through the bite of the female mosquitoes of the genus Anopheles. Identifying the disease accurately, depends on the method that is used in diagnosis. As such, a model that accurately detects malaria is needed to overcome the negative impacts of this disease on humans. To overcome these challenges, a Neuro Fuzzy Expert System for malaria diagnosis, designed to assist physicians in managing the uncertainties associated with malaria data and diagnosis is proposed. Neural network and Fuzzy Logic are both artificial intelligence tools that have established their reputation as alternative approaches to intelligent information processing systems. Both have certain advantages over classical methods, especially when vague data or prior knowledge is involved. However, the objective of this combination “Neuro-Fuzzy System” is to leverage the advantages of both techniques in detecting and classifying malaria parasite. PHP was used to build an integrated platform for timely and accurate detection of malaria and SQL as the database. By means of NFES, diagnosis of malaria becomes simple for medical practitioners/physicians, reduces the diagnostic time and cost. This project will discuss the design & proposed model involved in creating such a Neuro Fuzzy Expert System to diagnose malaria.