Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning.

ABSTRACT: Clinical prediction of advanced hepatic fibrosis (HF) and cirrhosis has long been challenging due to the gold standard, liver biopsy, being an invasive approach with certain limitations. Less invasive blood test tandem with a cutting-edge machine learning algorithm shows promising diagnostic potential. In this study, we constructed and compared machine learning methods with the FIB-4 score in a discovery dataset (n = 490) of hepatitis B virus (HBV) patients. Models were validated in an independent HBV dataset (n = 86). We further employed these models on two independent hepatitis C virus (HCV) datasets (n = 254 and 230) to examine their applicability. In the discovery data, gradient boosting (GB) stably outperformed other methods as well as FIB-4 scores (p < .001) in the prediction of adva

SUBMITTER: Wei R 

PROVIDER: S-EPMC6154783 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature