Fig. 1

Prediction of IgG4-RD diagnosis in patients with rheumatic diseases requiring differentiation by a CART, when the serum IgG4 level was known. A Decision tree algorithm. The blue color in the figure indicates the predicted percentage of IgG4-RD cases, and the red color indicates the percentage of non-IgG4-RD cases. The CART tree model revealed that the key process fluctuations leading to the diagnosis of IgG4-RD in this process were serum levels of IgG4, CRP, IgM, sIL-2R, C3, lymphocyte, and IgG. Furthermore, from top to bottom along the branch to each leaf node of the tree, the “if-then” rules could be generated to predict the diagnosis. For example, the right branch of the CART tree indicated that if serum IgG4 level was ≥151.5 mg/dL, CRP was <5 mg/dL, and IgM was <177.5 mg/dL, it was shown that IgG4-RD is significantly more likely than non-IgG4-RD. B ROC curve in the decision tree algorithm (left). The accuracy, sensitivity, and specificity of the algorithm were 0.917, 0.963, and 0.789, respectively, and the AUC was 0.889. C ROC curve for the decision tree algorithm (validation) (right). The validation of this algorithm showed that its accuracy, sensitivity, and specificity were 0.906, 0.983, and 0.714, respectively, and the AUC was 0.906