## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Author, Year, Region** & **Study Design** & **Population and Sample Size** & **AI Approaches** & **Implementation Classification and Comparator** & **Main Findings** & **Outcomes** \\ \hline Birri Makota and Musene 2023, Musene 2023, Southern Africa [42] & Retrospective cross-sectional study. & Males and females living with and without HIV (Aged with 15-49 females, 15-54 males, n = 6672). & Supervised classification-based machine learning approach. & Methodological, no comparator specified. & Predicted HIV classification-based ML models. & Predictive performance for HIV infection diagnosis. \\ \hline [PERSON] 2022, East Africa [43] & Retrospective observational study. & Males and females (n = 6346). & Random forest, XGBoost, and artificial neural networks. & Methodological, no definite, random forest, XGBoost, and mobile testing, and VCT. & Identified HIV knowledge as the most significant & Mean absolute error (MAE) for random comment forest, XGBoost, and artificial neural networks. & Mean absolute error (MAE) for random comment forest, XGBoost, and artificial neural networks. \\ \hline [PERSON] et al., 2022, Southern Africa [44] & Retrospective observational study. & Males and females (n = 20,577). & Random forest, SVM, and logistic regression. & Methodological, no comparator specified. & Modified high-risk individuals for target HIV prevention and screening strategies. \\ \hline [PERSON] et al., 2022, Southern Africa [44] & Retrospective observational study. & MSM (n = 1538). & DI, and ML algorithms: RNN, bagging classifier, gradient boosting classifier, SVM, and Gaussian naive Bayes classifier. & RNNs predicted HIV status with high precision, recall, and F1-scores, significantly improving early screening. & Precision, recall, accuracy, F1-score, and AUC for RNN and ML models. \\ \hline [PERSON] et al., 2023, Southern Africa [45] & Observational retrospective study. & ARI patients (median age 33 years, n = 264,877). & Adaptive boosting, categorical boosting, logistic regression, and gradient boosting. & ML model for patient retention on patient retention on ART successfully validated and extended. & Interruptions in ART prediction, prediction, validated and predicted value, and F1-scores. \\ \hline [PERSON] et al., 2022, West Africa [46] & Observational retrospective retrospective & Males and females (aged 15-49 years; n = 158,48). & Geodetector and LASSO regression. & Methodological, no comparator specified. & HCV prevalence, patients and spatial-temporal regional hotspots over time. \\ \hline [PERSON] et al., 2023, East Astrica [47] & Observational retrospective study. & HIV-positive adults receiving treatment (aged 15-18 years, n = 5264). & KNN, random forest, decision tree, gradient boosting, XGBoost, logistic regression, and SVM. & Random forest classifier best predictor with high sensitivity and AUC. \\ \hline [PERSON] et al., 2022, Southern Africa [48] & Observational longitudinal study. & Males and females (median age 39 years; n = 809,977). & Logistic regression, random forest, AdaBoost. & Methodological, no comparator specified. & ML models correctly identified HIV patients at risk for disengagement and unspressed viral \\ \hline [PERSON] 2023, East Africa [50] & Retrospective study. & Males and females (aged 20-39; n = 14,922). & Random forest, XGBoost, and artificial neural networks. & Random forest, manual HIV index case testing. & Random forest algorithm outperformed others in precision, recall, and F1-scores. & Development of HIV status predictive models for effective case testing. \\ \hline [PERSON] et al., 2023, SSA Regions [51] & Observational study. & Males and females (n = 302,355). & Unsupervised machine learning. & Methodological, no comparator specified. & Identified clustered predictors of HIV positivity using unsupervised ML. & Identification of HIV predictors and high-risk clusters. \\ \hline [PERSON] et al., 2021, East and Southern Africa [52] & Observational study. & Males and females (n = 87,044). & Elastic net, KNN, random forest, SVM, XGBoost, and light gradient boosting. & Methodological, no comparator specified. & XGBoost & Improved predictive accuracy for HIV positivity. \\ \hline [PERSON] et al., 2019, Southern Africa [53] & Retrospective cross-sectional study. & Women aged 15-49 years (n = 7808). & Decision tree and logistic regression. & Methodological, no comparator specified. & Decision tree showed higher sensitivity in HIV specificity, and status classification than logistic regression. & Sensitivity, specificity, and accuracy. \\ \hline \hline \end{tabular}
\end{table}