Prediction of the degree of liver fibrosis using different pattern recognition techniques

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.