Table 2. Key to model structure names.

Models trained in the manuscript are named in the form: <classes-predicted>.<algorithm>.<number-of-probes>, where classes-predicted is one of the options specified under Model Complexities and algorithm is one of the options specified under Model Algorithms. Thus, the model named six.rf.25 indicates a six-class multinomial random forest model based on 25 microarray probes. For comparison, two external models have been included, as indicated under External Models.

Model Complexities
Classes Predicted	Description
six	Six-class multinomial model, predicts the following classes
	[TB.HIV+, TB.HIV-, LTB.HIV+, LTB.HIV-, OD.HIV+, OD.HIV-]
four	Four-class multinomial model, predicts the following classes
	[TB.HIV+, TB.HIV-, LTB.HIV+, LTB.HIV
twopos	Two-class binary model, predicts TB or LTB, trained on HIV+ samples only
twoneg	Two-class binary model, predicts TB or LTB, trained on HIV- samples only
Model Algorithms
Algorithm	Description
glmnet	Logistic regression with elastic-net regularization (L1 and L2)
knn	K-nearest neighbours
nnet	Neural network
rf	Random forest
svmRadial	Support vector machine with radial basis function kernel
xgbTree	Extreme gradient boosting
External Models
External Model Name	Description
threeGene	Three-gene TB diagnostic signature, published by Sweeney et al (2016), consisting of the genes GBP5, KLF2 and DUSP3.
ACS	Signature of risk TB progression derived from South African adolescents with latent TB. Based of splice-junctions expression from 16 genes.