The AUC performance of models along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the random forest classifier and FIB-4 on the test set. The FIB-4 threshold of 2.67 is used for FIB-4 classification.

The AUC performance of models along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the random forest classifier and FIB-4 on the test set. The FIB-4 threshold of 2.67 is used for FIB-4 classification.本数据集涵盖测试集上随机森林分类器（random forest classifier）与FIB-4的受试者工作特征曲线下面积（Area Under the Curve，简称AUC）性能，同时包含灵敏度（sensitivity）、特异度（specificity）、阳性预测值（positive predictive value，简称PPV）以及阴性预测值（negative predictive value，简称NPV）。本次FIB-4分类所采用的阈值为2.67。