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Fig. 2 | Arthritis Research & Therapy

Fig. 2

From: The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches

Fig. 2

Performance of four JIA prediction models in the training and testing cohorts using only European samples. a Mean accuracy, sensitivity, and specificity for four models in training as assessed by tenfold cross-validation. Accuracies ranged from 59 to 74%, with gSVM having the highest accuracy. b Similar performance is reflected in the ROC analysis of the training cohort. gSVM again had the best performance with an AUC of 0.72. c Improved accuracy, sensitivity, and specificity for four models are noted in the testing cohort as assessed by true predictions of testing samples. KNN, cSVM, and gSVM all achieved a testing accuracy of 91%. d All models had AUCs > 0.90 in the testing cohort

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