Table 7 Result of e_inc_100k variable performances.

Model	Accuracy (%)	Precision (%)	Recall (%)	F1 Score (%)	ROC AUC Score (%) ^u
XGB	99.70	99.80	99.60	99.70	99.70
CB	99.70	99.60	99.80	99.70	99.69
RF	99.59	99.80	99.40	99.60	99.60
BC	99.59	99.80	99.40	99.60	99.60
ET	99.49	99.60	99.40	99.50	99.49
AB	99.29	99.40	99.20	99.30	99.29
GB	99.19	99.60	98.80	99.20	99.19
DT	98.58	98.40	98.80	98.60	98.57
KNN	65.65	66.07	66.33	66.20	65.64
SVM	60.26	68.37	40.28	50.69	60.55
GNB	50.91	63.79	7.41	13.29	51.54
LR	50.71	50.71	100.0	67.30	50.00
SGDC	49.29	0.00	0.00	0.00	50.00

Table shows e_inc_100k variable performances using the WHO TB burden dataset published in 2023 to predict TB Incidence and HIV-TB co-infection.
GB: Gradient Boosting, CB: CatBoost, XGB: XGB, ET: Extra Trees, RF: Random Forest, AB: AdaBoost, BC: Bagging Classifier, DT: Decision Tree, KNN: K-Nearest Neighbors, LR: Logistic Regression, SVM: Support Vector Machine, SGDC: Stochastic Gradient Descent Classifier, GNB: Gaussian Naive Bayes, ^u Receiver Operating Characteristic - Area Under the Curve.

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