Random Forest modelling and empirical evaluation of many different combinations led to selection of a complex panel, used in combination with the algorithm (300 × IFIT3) × (3 × SAMD9L) + GBP1 + IFITM3 + SNX10 + CALCOCO2), which showed good performance for discrimination of all TB disease groups from all controls (ROC = 0.9552, % sensitivity = 90.43, % specificity = 83.97). Here, IFIT3 is linked to tuberculosis.