Skip to main content
Fig. 3 | BMC Infectious Diseases

Fig. 3

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

Fig. 3

The performance of different diagnostic models established by machine learning for discriminating ATB patients from LTBI individuals in discovery cohort. 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. Cleveland dot plot showing the importance of various indicators in contributing to the model. ATB: active tuberculosis; LTBI: latent tuberculosis infection; ROC: receiver operator characteristics; AUC: area under the ROC curve

Back to article page

BMC Infectious Diseases

ISSN: 1471-2334

Contact us