Fig. 4
From: The differential diagnosis of IgG4-related disease based on machine learning
Prediction of IgG4-RD diagnosis in patients with rheumatic diseases requiring differentiation by a random forest, when the serum IgG4 level was unknown. A Decrease in Gini impurity. In the Random Forest method, the Gini impurity is an indicator of the importance of a variable. In this algorithm, the age at the first visit is the most important variable, followed by levels of serum IgA, sIL-2R, IgM, and IgE. B ROC curve for the random forest algorithm (left). The accuracy, sensitivity, and specificity of the algorithm were 0.897, 0.972, and 0.684, respectively, and the AUC was 0.955. C ROC curve for the random forest algorithm (validation) (right). The validation of this algorithm showed that its accuracy, sensitivity, and specificity were 0.877, 1.000, and 0.524, respectively, and the AUC was 0.925