A Fuzzy Expert System for Diagnosing and Analyzing Human Diseases
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According to the World Health Organization (WHO), human disease results in at least 70% of deaths every year. Approximately, 56 million people died in 2012 and 68% of all deaths in 2012 were as a result of noncommunicable diseases. The aim of this paper is to design and develop a webbased fuzzy expert system that would diagnose some of these diseases and provide users with expert advice and prescriptions based on the diagnosis generated by the system. The system would not only indicate if the disease is present but will also indicate the level at which the disease is present. The system is designed to diagnose five diseases which include asthma, diabetes, hypertension, malaria and tuberculosis. The system uses Mamdani inference method which has four phases: fuzzification, rule evaluation, rule aggregation and defuzzification. The fuzzy expert system was designed based on clinical observations and the expert knowledge. Having performed the experimentation and obtained relevant results, it is worthy of note that this approach of diagnosing human diseases has put the accuracy and reliability to 97%. It is the strong opinion of the authors that its full-scale implementation will assist in no small measure in carrying out same function in some of the hospitals and health institutions.
Disease Expert system Fuzzy logic Mamdani , Disease , Expert system
Azeez, N.A, Towolawi,T, Vyver, 0C.V, S. Misra, A. Adewumi, R. Damaševičius and R. Ahuja (2019) "A Fuzzy Expert System for Diagnosing and Analyzing Human Diseases" Springer Nature Switzerland AG 2019 A. Abraham et al. (Eds.): IBICA 2018, AISC 939, pp. 1–11, 2019. https://doi.org/10.1007/978-3-030-16681-6_47