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Automated Intelligent Diagnostic Liver Disorders Based on Adaptive Neuro Fuzzy Inference System and Fuzzy C-Means Techniques
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Liver disorders are most common in the world in recent times. In this study, an automated intelligent diagnostic approach has been proposed to indicate the liver disease by various sorts and separating facts of the disease using Adaptive Neuro Fuzzy Inference System (ANFIS) and Fuzzy C-Means (FCM) techniques. The data required to study has been chosen by more complex Neuro Fuzzy Model before its inspection on the clinical data. In order to ensure the adeptness of the physician, diagnosing the liver disease and prescribing the absence of sensational ways is very energetic assignment. To make the process more meaningful and scientific a data of about 583 patients, who were undergoing treatment of the doctors in various hospitals, is collected. Since the study includes the detailed information of the patient, so pre-processing was done. The Neuro Fuzzy techniques have been applied over the patient data. The results of these valuation show that Neuro Fuzzy technique can be applied successfully for advising the anesthetic for liver disease patient.
Keywords
AI, ANFIS, FCM, Machine Learning, Neuro Fuzzy.
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- UK national statistics, http://www.statistics.gov.uk/ [online].
- J. E. Everhart, and C. E. Ruhl, “Burden of digestive diseases in the United States Part III: Liver, biliary tract, and pancreas,” Gastroenterology, vol. 136, no. 4, pp. 1134-1144, 2009.
- https://www.hopkinsmedicine.org/healthlibrary/conditions/liver_biliary_and_pancreatic_disorders/liver_disease_statistics_85,P00686 [online].
- https://www.worldlifeexpectancy.com/india-liver-disease [online].
- C. J. L. Murray, and A. D. Lopez, The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries and Risk Factors in 1990 and Projected to 2020, Cambridge, Harvard University Press (Global Burden of Disease and Injury Series), vol. 1, 1996.
- M. F. bin Othman, and T. M. S. Yau, “Neuro fuzzy classification and detection technique for bioinformatics problems,” First Asia International Conference on Modelling & Simulation (AMS’07), Phuket, pp. 375-380, IEEE, 2007.
- A. Q. Ansari, and N. K. Gupta, “Automatic diagnosis of asthma using neuro-fuzzy system,” Fourth International Conference on Computational Intelligence and Communication Networks, pp. 819-823, IEEE, 2012.
- S. Chattopadhyay, “A neuro-fuzzy approach for the diagnosis of depression,” Applied Computing and Informatics, vol. 13, no. 1, pp. 10-18, January 2017.
- R. Sampath, and A. Saradha, “Alzheimer’s disease classification using Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) classifier,” Research Journal of Applied Sciences, Engineering and Technology, vol. 10, no. 1, pp. 29-34, 2015.
- C. Geetha, and D. Pugazhenthi, “Classification of alzheimer’s disease subjects from MRI using Fuzzy neural network with feature extraction using discrete wavelet transform,” Biomedical Research, special issue: s14-s21, 2018.
- https://www.kaggle.com/uciml/indian-liver-patient-records [online].
- J. Vieira, F. Morgado-Dias, and A. Mota, “Neuro-fuzzy systems: A survey,” 2004.
- FCM, http://www.mathworks.in/help/fuzzy/fcm.html [online].
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