Fuzzy Logic Based Expert System in the Diagnosis of Ebola Virus

  • Rasheed JIMOH University of Ilorin
  • Adekunle Andrew Afolayan Department of Computer Science, Kwara State College of Education, Oro
  • Joseph Bamidele Awotunde Department of Computer Science, University of Ilorin, Ilorin
  • Opeyemi Emmanuel Matiluko Centre for Systems and Information Services, Landmark University, Omu-Aran, Kwara State
Keywords: Ebola virus, Expert system, Diagnosis, Fuzzy logic, Medical


This study is aimed at bringing to the fore the importance of adapting expert system (ES) Ebola Virus diagnosis thereby showcasing the effectiveness of the fuzzy logic systems in the medical diagnosis of Ebola virus, to create a background in the identification of the applicable aspects in a fuzzy logic system. The combination of inadequate knowledge and in few cases the imprecise symptomatology, which exemplifies Ebola virus, exponentially increase the misdiagnosis of other diseases to be Ebola virus. The task of medical diagnosis for accuracy and precision may sometimes become very multifaceted and cumbersome. The physician with limited experience therefore faced the challenge of examining, diagnosing, and managing such conditions to better understand these perplexing symptoms so as to simplify timely and precise diagnosis. The medical expert system takes user input and depending on the input (symptoms) of the patient, diagnoses if the patient is suffering from Ebola virus or not. The System categorizes the Ebola symptoms using four linguistic variables based on the existing symptoms. Fuzzy rules are expressed and applied by java programming language model for the Ebola diagnosis system. The system provides useful evidence and anticipated data to develop fuzzy logic based control system for enhancement of the diagnosis of Ebola virus in real time application. Further work is expected to design, develop and implement the use of factor analysis and data mining techniques for classification and evaluation of qualified symptoms used in the process of Ebola diagnosis.


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How to Cite
JIMOH, R., Afolayan, A. A., Awotunde, J. B., & Matiluko, O. E. (2020). Fuzzy Logic Based Expert System in the Diagnosis of Ebola Virus. Ilorin Journal of Computer Science and Information Technology, 2(1), 73 - 94. Retrieved from https://iljcsit.com.ng/index.php/ILJCSIT/article/view/20