Abstract
Hepatitis, is one of the most common and dangerous diseases which affects liver. If hepatitis does not detect early, some side effects such as cirrhosis, hepatocellular carcinoma, liver failure and mature death will be occurred. Among different types of this disease, hepatitis C arises from HCV viruses, is the leading cause of liver disease. Although hepatitis C can be easily diagnosed by a simple test, the intensity rate of this disease is a qualitative and controversial issue. This paper attempts to design a fuzzy expert system for diagnosing the intensity rate of hepatitis C with FibroScan results. The proposed system includes three steps: pre-processing, create the primary fuzzy system and optimize the membership functions’ parameters. KNN method is used for filling missing data; moreover, feature selection is done by decision tree and genetic algorithm. The primary fuzzy system is established and in the third step, three different evolutionary algorithms are implemented to optimize the parameters of primary system. Results portray that Differential Evolution algorithm presents better performance in learning the pattern of data and decreases the error around 30%.
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- 1.
Mean Squared Error.
- 2.
Root Mean Square Error.
- 3.
Adaptive Neuro Fuzzy Inference System.
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Behrooz, M., Zarandi, M.H.F. (2018). Design and Implementation of Fuzzy Expert System Based on Evolutionary Algorithms for Diagnosing the Intensity Rate of Hepatitis C. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_3
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