استفاده از تکنیک‌های هوش مصنوعی برای شناسایی میزان شدت بیماری کبد چرب غیر الکلی توسط شاخص‌هاي بالینی

Jamali RJA. Non-alcoholic fatty liver disease. Journal of Kashan University of Medical Sciences 2010;14:169-81.[Persian].

Sug H, editors. Improving the prediction accuracy of liver disorder disease with oversampling. proceeding of the 6th wseas international conference on Computer Engineering and Application, and American conference on Applied Mathematics; 2012; Cambridge 2012.

Cales P, Boursier J, Chaigneau J, Laine F, Sandrini J, Michalak S, et al. Diagnosis of different liver fibrosis characteristics by blood tests in non-alcoholic fatty liver disease. Liver International 2010;30:1346-54. doi: 10.1111/j.1478-3231.2010.02314.x

Fujiwara Sh, Hongou Y, Miyaji K, Asai A, Tanabe T, Fukui H, et al. Relationship between liver fibrosis noninvasively measured by fibro scan and blood test. Bulletin of the Osaka Medical College 2007;53:93-105.

Janda JM ASL, editors. The Entrobacteria. 2nd ed. Washington: ASM press;2006.

Afdhal NH. Fibroscan (transient elastography) for the measurement of liver fibrosis. Gastroenterol Hepatol 2012;8:605-7.

Wai Ch, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conjeevaram HS. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology 2003;38:518-26. doi: 10.1053/jhep.2003.50346

Lok AS, Ghany MG, Goodman ZD, Wright EC, Everson GT, Sterling RK, et al. Predicting cirrhosis in patients with hepatitis c based on standard laboratory test: results of the halt-c cohort. Hepatology 2005;24:282-92. doi: 10.1002/hep.20772

Forns X, AmpurdanesS, Llovet JM, Aponte J, Quinto L, Martinez-Bauer E, et al. Identification of chronic hepatitis C patients without hepatic fibrosis by a simple predictive model. Hepatology 2002;36:986-92. doi: 10.1053/jhep.2002.36128

Angulo P, Hui J, Marchesini G, Bugianesi E, George J, Farrel G, et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007;45:846-54. doi: 10.1002/hep.21496

Pournik O, Dorri S, Zabolinezhad H, Alavian SM, Eslami S. A diagnostic model for cirrhosis in patients with non-alcoholic fatty liver disease: an artificial neural network approach. Med J Islam Repub Iran 2014;28:116.

Roobeart D, Karakoulus G, editors. Information gain, correlation and support vector machine. In: Guyon I, Gunn S, editors. Feature extraction. Berlin: Springer-Verlag Berlin Heidelberg;2006.p.463-70. doi: 10.1007/978-3-540-35488-8

Cano A, Nguyen D, Ventura S, Cios K. ur-CAIM: improved CAIM discretization for unbalanced and balanced data. Soft Computing 2016;20:173-88. doi: 10.1007/s00500-014-1488-1

Antkowiak M. Artificial neural networks vs. Support vector machines for skin diseases recognition [dissertation]. Sweden: Umea Univ.;2006.

El Farissi I, Azizi M, Lanet J-L, Moussaoui M. Neural network vs. Bayesian network to detect Java card mutants. AASRI Procedia 2013;4:132-7. doi: 10.1016/j.aasri.2013.10.021

Ganesan N, Venkatesh K, Rama M, Palani AM. Application of neural networks in diagnosing cancer disease using demographic data. International Journal of Computer Applications 2010;1:76-85.

Jaleel JA, Salim S, Aswin R. Artificial neural network based skin cancer detection. International Journal of Advanced Research in Electrical 2012;1:200-5.

Khan IY, Zope P, Suralkar S. Importance of artificial neural network in medical diagnosis disease like acute nephritis disease and heart disease. International Journal of Engineering Science and Innovative Technology (IJESIT) 2013;2:210-7.

Cohen WW, editor. Fast effective rule induction. Proceedings of the 12th international conference on machine learning;1995; Tahoe City, California.

Quinlan JR. Learning with continuous classes. 5th Australian joint conference on artificial intelligence; 1992: Singapore.