Main Article Content
The present populace of India is 1,362,255,678 as of January, 2019, in view of the most recent United Nations estimates . Worldwide examinations calculate that there are 8.7 million individuals living with chronic Hepatitis in India. Chronic Hepatitis disease represents 12-32 per cent of liver malignant growth and 10-20 per cent of cirrhosis cases in India. The vast majority with constant Hepatitis B or C are ignorant of diseases and are at genuine danger of creating cirrhosis or liver malignant growth.Machine learning fits a few procedures superior to other traditional methods like Biopsy. This paper evaluate various machine learning methods to predict advanced liver fibrosis by using patient’s blood report to build up the optimization and classification models. The METAVIR score is a device used to assess the seriousness of fibrosis seen on a liver biopsy test from a human who has chronic hepatitis. Based on the METAVIR score [2,3,4] chronic hepatitis divided into three parts, first one is classified as mild stage, second one is moderate stage and third one is advanced stage of fibrosis. Grey Wolf Optimization, Random Forest Classifier and Decision tree procedure models forpropelled fibrosis chance expectation were produced. ROC curve and confusion matrix was evaluated to compare the accuracy of proposed methods.
Bedossa B, Carrat F. Liver biopsy: The best, not the gold standard, J. Hepatology. 2009; 50:1–3.
Crisan D, et al. Prospective non-invasive follow-up of liver fibrosis in patients with chronic hepatitis C,” J. Gastrointest Liver Dis. 2012;21:375–382.
Hashem S, et al. A Simple multi-linear regression model for predicting fibrosis scores in chronic Egyptian hepatitis C virus patients, Int. J. Bio-Technol. Res. 2014;4(3):37–46.
Alodini Q. Prevalence of Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) infections among blood donors at Al-Thawra Hospital Sana’a City-Yemen,” Yemeni J. Med. Sci. 2012;6:16–20.
Hashem S, et al. Accurate prediction of advanced liver fibrosis using the decision tree learning algorithm in chronic Hepatitis C Egyptian patients, Gastroenterology Res. Practice. 2016;2016(2636390.63).
Parkes J, Guha IN, Roderick P, Rosenberg W. Performance of serum marker panels for liver fibrosis in chronic hepatitis C, J. Hepatol. 2006;44:462–474.
Gravitz L. A smouldering public-health crisis, Nature. 2011;474(7350):S2–S4.
Bonacini M, Hadi G, Govindarajan S, Lindsay KL. Utility of a discriminant score for diagnosing advanced fibrosis or cirrhosis in patients with chronic hepatitis C virus infection, Am. J. Gastroenterology. 1997;92: 1302–1304.
Castera L. Noninvasive methods to assess liver disease in patients with hepatitis B or C,” Gastroenterology. 2012;142:1293–1302.
Kim MY, Jeong WKY, Baik SK. Invasive and noninvasive diagnosis of cirrhosis and portal hypertension, World J. Gastroenterology. 2014;20(15):4300–4315.
Nashaat E. Lipid profile among chronic hepatitis C Egyptian patients and its levels pre and post treatment, Nature Sci. 2010;8(7):83–89.
Regev A, et al. Sampling error and intra-observer variation in liver biopsy in patients with chronic HCV infection, Am. J. Gastroenterology. 2002;97:2614–2618.
Rosen H. Clinical practice, chronic hepatitis C infection, New England J. Med. 2011; 364(25):2429–2438.
Sterling RK, et al. Development of a simple non-invasive index to predict significant fibrosis in patients with HIV/ HCV co-infection, Hepatology. 2006;43:1317–1325.