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Fig. 5 | BMC Infectious Diseases

Fig. 5

From: Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

Fig. 5

The diagnostic performance of the established 28 models for differentiating ATB patients from LTBI individuals in A training set, B test set, and C validation set. The height and color of the column represented the value of performance parameters after normalization to range between 0 and 1. acc: accuracy; auc: area under the ROC curve; bacc: balanced accuracy; bbrier: binary brier score; ce: classification error; dor: diagnostic odds ratio; fbeta: F-beta score; fdr: false discovery rate; fn: false negatives; fnr: false negative rate; fomr: false omission rate; fp: false positives; fpr: false positive rate, mbrier: multiclass brier score; mcc: matthews correlation coefficient; npv: negative predictive value; ppv: positive predictive value; prauc: area under the precision-recall curve; tn: true negatives; tnr: true negative rate; tp: true positives; tpr: true positive rate

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