@inproceedings{b1d2955729254a6080c43cca42136720,
title = "Prediction of the degree of liver fibrosis using different pattern recognition techniques",
abstract = "Liver biopsy is considered as mandatory for the management of patients infected with the hepatitis C virus (HCV), particularly for staging of fibrosis degree. However, due to its invasive nature and limitations of sampling error, the tendency is to substitute the liver biopsy with non-invasive method. The objective of this study is to combine the serum biomarkers and histopathological findings to develop a classification model that can predict the hepatic fibrosis stage. The best developed classification model was able to predict the different fibrosis grades with accuracy of 93.7%. This accuracy represents a substantial improvement over previous works and would pave the way to utilize classification models as a clinically non-invasive and reliable method to assess the degree of liver fibrosis.",
author = "Hashem, {Ahmed M.} and Rasmy, {M. Emad M.} and Wahba, {Khaled M.} and Shaker, {Olfat G.}",
year = "2010",
doi = "10.1109/CIBEC.2010.5716043",
language = "English",
isbn = "9781424471706",
series = "Cairo International Biomedical Engineering Conference, CIBEC, Proceedings",
publisher = "IEEE",
pages = "210--214",
booktitle = "2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010",
note = "2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010, CIBEC 2010 ; Conference date: 16-12-2010 Through 18-12-2010",
}