Abstract: Objective ·using Youden index, to improve the performance of the hepatic fibrosis diagnostic models, and to solve the problem of unbalanced diagnostic sensitivity when there is a big difference in the sample size of two groups. Methods · Two hepatitis B virus (HBV) datasets available on GitHub were selected, including 482 HBV infected subjects recruited Shuguang Hospital in affiliation with Shanghai University of Traditional Chinese Medicine (train set) and 86 HBV infected subjects Xiamen Hospital of Traditional Chinese Medicine (validation set).using the two datasets, linear discriminant analysis model, random forest model, gradient boosting model and decision tree model were established, based on four clinical parameters (age, glutamic-oxaloacetic transaminase, glutamic-pyruvic transaminase, and platelet count) of patients, for the diagnosis of early and advanced hepatic fibrosis as well as the diagnosis of hepatic fibrosis and cirrhosis. Youden index was used to adjust the threshold value and the classification result of each diagnostic model. The diagnostic performances of each machine learning model and fibrosis index based on the 4 factor (FIB-4) were evaluatedaccuracy, the area under the receiver operating characteristic curve (AUC) and sensitivity. Results · The intergroup sensitivity imbalance occurred in all machine learning models. After using Youden index, the difference of intergroup sensitivity was greatly reduced, and the total accuracy and AUC values of machine learning models were generally higher than those of FIB-4 index. Conclusion · The improved diagnostic models based on Youden index can reduce the difference of intergroup sensitivity and improve the comprehensive performance of the diagnostic models of hepatic fibrosis.

Key words: Youden index, machine learning, liver fibrosis, disease diagnosis