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Fig. 2 | Arthritis Research & Therapy

Fig. 2

From: The differential diagnosis of IgG4-related disease based on machine learning

Fig. 2

Prediction of IgG4-RD diagnosis in patients with rheumatic diseases requiring differentiation by a random forest, when the serum IgG4 level was known. A Decrease in Gini impurity. In this algorithm, the serum IgG4 concentration is the most important variable, followed by the age at the first visit, levels of serum IgA, sIL-2R, and IgM. B ROC curve for the random forest algorithm (left). The accuracy, sensitivity, and specificity of the algorithm were 0.938, 0.981, and 0.816, respectively, and the AUC was 0.986. C ROC curve for the random forest algorithm (validation) (right). The validation of this algorithm showed that its accuracy, sensitivity, and specificity were 0.938, 1.000, and 0.762, respectively, and the AUC was 0.974

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