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Fig. 4 | BMC Infectious Diseases

Fig. 4

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

Fig. 4

The validation of diagnostic models established for discriminating ATB patients from LTBI individuals. Scatter plots showing predictive values of diagnostic models (A cforest; B bart; C gamboost; D gbm; E glmnet; F lda; G log_reg; H svm) in ATB patients and LTBI individuals. Horizontal lines indicate the median. ***P < 0.001 (Mann–Whitney U test). Blue dotted lines indicate the cutoff value (0.5) in segregating these two groups. ROC curves showing the performance of diagnostic models (A cforest; B bart; C gamboost; D gbm; E glmnet; F lda; G log_reg; H svm) in segregating ATB patients from LTBI individuals. Tree and leaf plots showing predictive value of each participant when displaying as cluster distribution. The size of circle represents the predictive value. ATB: active tuberculosis; LTBI: latent tuberculosis infection; ROC: receiver operator characteristics; AUC: area under the ROC curve

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BMC Infectious Diseases

ISSN: 1471-2334

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