Abstract: Approximately 3% to 10% of hepatitis C cases can develop to hepatocellular carcinoma after viral hepatitis C virus infection. Worldwide, 27% of cirrhosis statistics are due to hepatitis C and 25% are due to hepatocellular carcinoma. Accurate prediction of hepatitis C infection is a matter of urgency. Machine learning is fast and accurate. Hepatitis research often used time series analysis or pathological analysis in the past and did not use machine learning algorithms as an auxiliary diagnosis method for hepatitis C. To select the optimal model for detecting hepatitis C, different machine learning models are compared and analyzed in UCI(University of California Irvine) hepatitis C data. The experimental results show that gradient boosting tree, random forest and light gradient boosting machine perform better, among which the gradient boosting tree is accurate in predicting hepatitis C up to 0. 935 1. The most accurate prediction of hepatitis C infection is performed using gradient boosting tree.

Key words: hepatitis C； , machine learning； , gradient boosting decision tree； , light gradient boosting machine