Version 1
: Received: 25 September 2023 / Approved: 25 September 2023 / Online: 26 September 2023 (05:26:07 CEST)
How to cite:
Molamasoumi, A.; Sadeghizadeh, H.; Rajabi, M.; Hajam, M. A.; Khanday, A. M. U. D. Neuro Fuzzy GreyWolf Optimisation Method for Diagnosing the Liver Disorders. Preprints2023, 2023091704. https://doi.org/10.20944/preprints202309.1704.v1
Molamasoumi, A.; Sadeghizadeh, H.; Rajabi, M.; Hajam, M. A.; Khanday, A. M. U. D. Neuro Fuzzy GreyWolf Optimisation Method for Diagnosing the Liver Disorders. Preprints 2023, 2023091704. https://doi.org/10.20944/preprints202309.1704.v1
Molamasoumi, A.; Sadeghizadeh, H.; Rajabi, M.; Hajam, M. A.; Khanday, A. M. U. D. Neuro Fuzzy GreyWolf Optimisation Method for Diagnosing the Liver Disorders. Preprints2023, 2023091704. https://doi.org/10.20944/preprints202309.1704.v1
APA Style
Molamasoumi, A., Sadeghizadeh, H., Rajabi, M., Hajam, M. A., & Khanday, A. M. U. D. (2023). Neuro Fuzzy GreyWolf Optimisation Method for Diagnosing the Liver Disorders. Preprints. https://doi.org/10.20944/preprints202309.1704.v1
Chicago/Turabian Style
Molamasoumi, A., Mohd Asif Hajam and Akib Mohi Ud Din Khanday. 2023 "Neuro Fuzzy GreyWolf Optimisation Method for Diagnosing the Liver Disorders" Preprints. https://doi.org/10.20944/preprints202309.1704.v1
Abstract
The hybrid method proposed in the study, ANFIS-GWO, combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO) for the diagnosis of liver disorders. ANFIS is a powerful tool that combines the advantages of neural networks and fuzzy logic to create a hybrid model capable of handling complex and uncertain data. GWO is a metaheuristic optimization algorithm inspired by the social behaviour of grey wolves. In the ANFIS-GWO method, the hyperparameters of ANFIS are optimized using GWO. This optimization process aims to fine-tune the ANFIS model based on the available dataset, which consists of 7 characteristic attributes and 354 samples related to liver diseases. By adopting the hyper-parameters, the ANFIS-GWO method enhances the overall performance and accuracy of the diagnostic system. To evaluate the effectiveness of the ANFIS-GWO intelligent medical system, the study employs classification accuracy, sensitivity, and specificity analysis. Classification accuracy measures the overall correctness of the system in predicting liver disease cases. Sensitivity refers to the system’s ability to correctly identify individuals with liver disorders, while specificity measures its ability to correctly identify those without liver disorders. Experimental results demonstrate that the performance of the ANFIS-GWO method surpasses that of traditional Fuzzy Inference Systems (FIS) and ANFIS models that do not undergo an optimization phase. This suggests that the integration of GWO optimization significantly improves the diagnostic accuracy of the ANFIS model for liver disease diagnosis.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
