اخبار و اعلانات
آمار
| تعداد نشریات | 31 |
| تعداد شماره‌ها | 712 |
| تعداد مقالات | 6,935 |
| تعداد مشاهده مقاله | 11,408,818 |
| تعداد دریافت فایل اصل مقاله | 7,787,366 |
An improvement in integrating clustering method and neural network to extract rules and application in diagnosis support | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 11، دوره 19، شماره 5، آذر و دی 2022، صفحه 147-165 اصل مقاله (494.39 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2022.7162 | ||
| نویسندگان | ||
| V. D. Minh1؛ T. T. Ngan2؛ T. M. Tuan* 2؛ V. T. Duong1؛ N. T. Cuong1 | ||
| 1University of Industry, 298 Cau Dien street, Bac Tu Liem District, Hanoi, Viet Nam | ||
| 2Faculty of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam | ||
| چکیده | ||
| Most of chronic liver diseases without suitable treatment will lead to cirrhosis of the liver, eventually progressing to liver cancer. Thus, early diagnosis is very important in detecting the liver diseases and suggesting the treatment at the right time. A useful model that effectively predicts the patient's liver fibrosis has great importance in reducing the load on doctors, especially in lower-level hospitals. In this paper, a new model combining semi-supervised learning method and fuzzy min max neural network with selective fuzzy rule set rendering is proposed. Cirrhosis level is evaluated by APRI and FIB-4. The proposed method is experimented on data sets from machine learning databases, including UCI and CS. Apart from that, our method is also implemented on the liver data set collected from the hospitals of Thai Nguyen province. The comparison among our proposed method and other related ones is also given. The obtained results show that our proposed model has better performance than compared methods in terms of execution time and the number of rules. | ||
| کلیدواژه‌ها | ||
| Artificial neural network؛ semi-supervised clustering؛ chronic liver diseases؛ liver disease diagnosis؛ cirrhosis | ||
| مراجع | ||
|
[1] 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.
[2] 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.
[3] G. A. Carpenter, A. H. Tan, Rule extraction: From neural architecture to symbolic representation, Connection Science, 7(1) (1995), 3-27.
[4] 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.
[5] 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.
[6] 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.
[7] L. D. Jules, Chronic Hepatitis, Harrisons Gastroenterology and Hepatology, 17 th edit: Mc Graw Hill Medical, (2012), 390-414.
[8] T. T. Khuat, B. Gabrys, A comparative study of general fuzzy min-max neural networks for pattern classification problems, Neurocomputing, 386 (2020), 110-125.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] P. K. Simpson, Fuzzy min-max neural networks-Part 1: Classication, IEEE Transactions on Neural Networks, 3(5) (1992), 776-786.
[19] P. K. Simpson, Fuzzy min-max neural networks-Part 2: Clustering, IEEE Transactions on Fuzzy Systems, 1(1) (1993), 32-45.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] C. Zhong, M. Malinen, M. Miao, P. Fränti, A fast minimum spanning tree algorithm based on K-means, Information Sciences, 295 (2015), 1-17. | ||
|
آمار تعداد مشاهده مقاله: 381 تعداد دریافت فایل اصل مقاله: 372 |
||