Advances in Networks
Volume 7, Issue 2, December 2019, Pages: 45-50
Received: Oct. 20, 2019;
Accepted: Nov. 21, 2019;
Published: Nov. 27, 2019
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Labiga Laban Thomas, Department of Computer Science, Faculty of Physical Science, Modibbo Adama University of Technology, Yola, Nigeria
Ibrahim Goni, Department of Computer Science, Faculty of Science, Adamawa State University, Mubi, Nigeria
Gideon Daniel Emeje, Department of Computer Science, Faculty of Physical Science, Modibbo Adama University of Technology, Yola, Nigeria
Fuzzy logic lies in the ability to process nonlinear relationships. Because of the clinical complexity and pathologic heterogeneity of various diseases, correct identification of patients with active disease likely depends on the presence of a single defining feature. Hence, it is not surprising that standard linear statistical methodologies are relatively inadequate for medical diagnosis. In the medical field, dealing with diagnosis error and several levels of uncertainties and imprecision in the diagnoses of diseases had been a great challenge. To solve such problems, artificial intelligence gives a solution through expert system. Fuzzy Logic handles uncertainties, imprecisions and obscurity in decision making. Fuzzy logic is been preferred by Researchers because of its flexible structure and use of intuitive methods instead of specific algorithm. It deals with the degree of membership as it refers to the extent to which an event occurred or can occur. Fuzzy set uses the continuum of logical values between 0 and 1. Different Fuzzy models were reviewed. These systems diagnose many diseases such as: Malaria born infectious disease, Heart related diseases or cardiovascular diseases (like Atherosclerosis), cancer, Asthma, Lungs cancer, Cold and Flu, Hepatitis, Osteomyelitis and Meningitis. In the near future, medical service delivery will be more accessible and more efficient due to availability of Medical Diagnostic Systems.
Labiga Laban Thomas,
Gideon Daniel Emeje,
Fuzzy Models Applied to Medical Diagnosis: A Systematic Review, Advances in Networks.
Vol. 7, No. 2,
2019, pp. 45-50.
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