Table 8 Result of e_inc_tbhiv_100k variable performances.

Model	Accuracy (%)	Precision (%)	Recall (%)	F1 Score (%)	ROC AUC Score (%) ^u
GB	98.58	98.32	98.73	98.53	98.58
CB	98.58	98.94	98.1	98.52	98.56
XGB	98.27	98.31	98.1	98.2	98.27
ET	98.27	98.51	97.89	98.2	98.26
RF	98.07	98.72	97.26	97.98	98.04
AB	97.87	97.48	98.1	97.79	97.87
BC	97.76	98.29	97.05	97.66	97.74
DT	96.85	97.84	95.57	96.69	96.8
KNN	61.59	60.67	57.59	59.09	61.44
LR	51.83	0.00	0.00	0.00	50.00
SVM	50.51	49.18	82.7	61.68	51.64
SGDC	48.17	48.17	100.0	65.02	50.0
GNB	48.07	48.05	95.99	64.04	49.76

Table shows e_inc_tbhiv_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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