Fig. 3

Prediction of IgG4-RD diagnosis in patients with rheumatic diseases requiring differentiation by a CART, when the serum IgG4 level was unknown. 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 the age at the first visit, several serum biomarkers, and the peripheral counts of white blood cells and its fractions. For example, the right branch of the CART tree indicated that if age at the first visit ≥51.5 years, serum IgM level was <201 mg/dL, peripheral counts of leucocytes <10,960/μL, serum IgG level was ≥1,253.5 mg/dL, and serum IgA level was <289.5 mg/dL, it was shown that IgG4-RD is significantly more likely than non-IgG4-RD. B ROC curve for the decision tree algorithm (left). The accuracy, sensitivity, and specificity of the algorithm were 0.807, 0.869, and 0.632, respectively, and the AUC was 0.776. C ROC curve for the decision tree algorithm (validation) (right). The validation of this algorithm showed that its accuracy, sensitivity, and specificity were 0.852, 0.917, and 0.667, respectively, and the AUC was 0.763