Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection

•Machine learning was firstly evaluated for differentiating ATB from LTBI based on laboratory test data with large cohort including training set, test set, and validation set.•Six models including GBM, MARS, Bagging, GLM, NNET, and FDA were successfully established using machine learning.•GBM model was the promising tool with satisfactory diagnostic performance for discrimination between ATB and LTBI.