Fig. 4

The diagnostic panel development by machine learning. A The 30 top-ranked features were screened by random forest model, and they were ranked by accuracy (left) and Gini index (right); the features closer to the upper right were more important. B The correlation matrix between the top 10 features, including CLU, CA19-9, DBIL, IBIL, ALP, TBIL, GGT, TG, LDLC, and TBA; the numbers represent the correlation coefficient (r) between the two features. C ROC curves of the seven-panel and its members; the data means AUC (95%CI). D tSNE analysis of the seven-panel; the blue represents CCA, and the pink represents non-tumor. E The DCA analysis of the seven-panel, CLU and CA19-9; the green represents CA19-9, blue represents CLU, and red represents the seven-panel. F ROC curve of the seven-panel in the external validation set; the data means AUC (95%CI). AUC is the area under the curve. r ≥ 0.8 represents high correlation, 0.5 ≤ r < 0.8 represents strong correlation, 0.3 ≤ r < 0.5 represents weak correlation, and r < 0.3 represents no correlation