P. Agrawal, V. Madaan, V. Kumar, Fuzzy rule-based medical expert system to identify the disorders of eyes, ENT and liver, International Journal of Advanced Intelligence Paradigms, 7(3-4) (2015), 352-367.
 Z. A. Bulaghi, A. H. Navin, M. Hosseinzadeh, A. Rezaee, World competitive contest-based artificial neural network: A new class-specific method for classification of clinical and biological datasets, Genomics, 113(1) (2021), 541-552.
 G. A. Carpenter, A. H. Tan, Rule extraction: From neural architecture to symbolic representation, Connection Science, 7(1) (1995), 3-27.
 Y. Chen, X. Yue, H. Fujita, S. Fu, Three-way decision support for diagnosis on focal liver lesions, Knowledge-based Systems, 127 (2017), 85-99.
 K. S. Darne, S. S. Panicker, Use of fuzzy C-mean and fuzzy min-max neural network in lung cancer detection, International Journal of Soft Computing and Engineering (IJSCE), 3(3) (2013), 265-269.
 M. Eslam, S. K. Sarin, V. W. S. Wong, J. G. Fan, T. Kawaguchi, S. H. Ahn, J. George, The Asian pacific association for the study of the liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease, Hepatology International, (2020), 1-31.
 L. D. Jules, Chronic Hepatitis, Harrisons Gastroenterology and Hepatology, 17 th edit: Mc Graw Hill Medical, (2012), 390-414.
 T. T. Khuat, B. Gabrys, A comparative study of general fuzzy min-max neural networks for pattern classification problems, Neurocomputing, 386 (2020), 110-125.
 A. S. Kumar, A. Kumar, V. Bajaj, G. K. Singh, Class label altering fuzzy min-max network and its application to histopathology image database, Expert Systems with Applications, 176 (2021), 114880.
 B. N. Li, C. K. Chui, S. Chang, S. H. Ong, A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images, Expert Systems with Applications, 39(10) (2012), 9661-9668.
 M. F. Mohammed, C. P. Lim, Improving the fuzzy min-max neural network with a K-nearest hyperbox expansion rule for pattern classification, Applied Soft Computing, 52 (2017), 135-145.
 M. Ney, S. Li, B. Vandermeer, L. Gramlich, K. P. Ismond, M. Raman, P. Tandon, Systematic review with metaanalysis: Nutritional screening and assessment tools in cirrhosis, Liver International, 40(3) (2020), 664-673.
 B. R. Rajakumar, A. George, On hybridizing fuzzy min-max neural network and firefly algorithm for automated heart disease diagnosis, In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), (2013), 1-5.
 A. B. Ryerson, S. Schillie, L. K. Barker, B. A. Kupronis, C. Wester, Vital signs: Newly reported acute and chronic hepatitis C cases-United States, 2009-2018, Morbidity and Mortality Weekly Report, 69(14) (2020), 399.
 M. Seera, C. P. Lim, C. K. Loo, H. Singh, A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring, Applied Soft Computing, 28 (2015), 19-29.
 T. Sharma, G. Kumawat, P. Chakrabarti, S. Poddar, T. Chakrabarti, A. M. Kamali, M. Nami, et.al., Using artificial neural network and machine learning algorithms to scrutinize liver diseases, (2021). DOI: 10.21203/rs.3.rs324049/v1.
 S. Shinde, S. D. Waghole, M. M. Bare, P. P. Patil, P. M. Humnabade, Diabetes diagnosis using fuzzy min-max neural network with rule extraction and apriori algorithm, The International Journal of Science and Technoledge, 2(4) (2014), 369.
 P. K. Simpson, Fuzzy min-max neural networks-Part 1: Classication, IEEE Transactions on Neural Networks, 3(5) (1992), 776-786.
 P. K. Simpson, Fuzzy min-max neural networks-Part 2: Clustering, IEEE Transactions on Fuzzy Systems, 1(1) (1993), 32-45.
 A. Singh, J. C. Mehta, D. Anand, P. Nath, B. Pandey, A. Khamparia, An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced kmeans clustering and improved ensemble learning, Expert Systems, 38(1) (2021), e12526.
 A. Singh, B. Pandey, Intelligent techniques and applications in liver disorders: A survey, International Journal of Biomedical Engineering and Technology, 16(1) (2014), 27-70.
 R. K. Sterling, E. Lissen, N. Clumeck, R. Sola, M. C. Correa, J. Montaner, D. Messinger, Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection, Hepatology, 43(6) (2006), 1317-1325.
 T. N. Tran, D. M. Vu, M. T. Tran, B. D. Le, The combination of fuzzy min-max neural network and semisupervised learning in solving liver disease diagnosis support problem, Arabian Journal for Science and Engineering, 44(4) (2019), 2933-2944.
 D. M. Vu, V. H. Nguyen, B. D. Le, Semi-supervised clustering in fuzzy min-max neural network, In International Conference on Advances in Information and Communication Technology. Springer International Publishing, (2016), 541-550.
 C. T. Wai, J. K. Greenson, R. J. Fontana, J. D. Kalbfleisch, J. A. Marrero, H. S. Conjeevaram, A. S. F. Lok, A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C, Hepatology, 38(2) (2003), 518-526.
 J. Wang, C. P. Lim, D. Creighton, A. Khorsavi, S. Nahavandi, et. al., Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction, Neural Computing and Applications, 26(2) (2015), 277-289.
 Z. Yao, J. Li, Z. Guan, Y. Ye, Y. Chen, Liver disease screening based on densely connected deep neural networks, Neural Networks, 123 (2020), 299-304.
 C. Zhong, M. Malinen, M. Miao, P. Fränti, A fast minimum spanning tree algorithm based on K-means, Information Sciences, 295 (2015), 1-17.