Fig. 5From: Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infectionThe 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 rateBack to article page