. 2023 Feb 3;16:435–451. doi: 10.2147/IJGM.S397031

Table 2.

Comparison of Viral Load and CD4 Classification Models Using Accuracy and AUC

Algorithms	Comparison Method	Viral Load Models	CD4 Models
K Nearest Neighbours (KNN)	Accuracy (%)	83.0	71.0
K Nearest Neighbours (KNN)	AUC	0.92	0.76
Support Vector Machine (SVM)	Accuracy (%)	89.0	76.0
Support Vector Machine (SVM)	AUC	0.95	0.79
Logistic Regression (LR)	Accuracy (%)	80.0	75.0
Logistic Regression (LR)	AUC	0.88	0.79
Decision Tree (DT)	Accuracy (%)	91.0	65.0
Decision Tree (DT)	AUC	0.91	0.65
Gaussian Naive Bayes (GNB)	Accuracy (%)	78.0	72.0
Gaussian Naive Bayes (GNB)	AUC	0.85	0.75
Random Forest (RF)	Accuracy (%)	95.0	77.0
Random Forest (RF)	AUC	0.99	0.83
Gradient Boosting (GB)	Accuracy (%)	95.0	79.0
Gradient Boosting (GB)	AUC	0.98	0.83
eXtreme Gradient Boosting (XGB)	Accuracy (%)	96.0	76.0
eXtreme Gradient Boosting (XGB)	AUC	0.99	0.81

Abbreviation: AUC, area under curve.