# Utilization of Machine Learning Algorithms
## 4 Discussion  
### Main Findings  
This study examines different ML algorithms that have been employed in various studies across the globe aimed at enhancing the efficiency and accuracy of HIV testing interventions, and identifies key outcomes, successes, gaps, opportunities, and limitations associated with their implementation. This review included 51 studies, of which 21 were conducted in the Americas, 2 were from European countries, 4 were from Australia, 9 were from Asia, and 18 were from SSA. In terms of individual countries, the USA contributed the highest number of studies (\(n=18\)), followed by China (\(n=5\)), Zimbabwe (\(n=5\)), Ethiopia (\(n=5\)), Australia (\(n=4\)), and South Africa (\(n=3\)). Among the 18 studies conducted in SSA, 16 were in East and South Africa, while only 2 [46; 53] were conducted in West and Central African countries. Several factors could explain these disparities. The USA is one of the most developed countries globally, having achieved significant advancements in research and technology [72]. In addition, East and South Africa bear the highest global HIV burden [4], possibly explaining the concentration of HIV-related studies in these regions.  
Most studies utilized multiple SML models, with LR, RF, SVM, the LASSO model, XGBoost, DT, and DL being the most utilized algorithms, except for a few studies that employed UML. In contrast to UML, SML allows the researcher to easily understand the actual classes in the training data even prior to inputting the data for training [73]. Despite the challenges in classifying big data, SML provides more accurate results, making it the most popular ML approach used in various fields of research [74].  
The best-performing algorithms were XGBoost, RF, DL (RNN and MobileNet2-CNN), DT, GBM, SVM, LR, GCN, and monogram. In most studies, these models outperformed other models in terms of accuracy. The XGBoost model outperformed several algorithms, exhibiting high predictive performance toward strengthening HIV testing interventions. However, our findings contrast those of [PERSON] et al., who examined supervised automated machine learning (SAML), DL, and XGBoost and discovered that SAML had the most significant predictive performance [75]. The datasets used in their investigation were relatively small, which may explain the inconsistencies [75]. Furthermore, this review found that RF outperformed other models in predicting HIV and HIV testing. Random forest outscored SVM, XGBoost, LR, DT, and traditional approaches in various studies [13; 19; 36]. This is supported by [PERSON] et al. [76], who conducted a study comparing various ML models for predicting virological failure in HIV patients. In their study, RF demonstrated the highest accuracy [76]. Deep learning models such as RNN, PLS-DA, and CNN (MobileNet2) were also identified as the best-performing algorithms in a few studies [27; 40; 41; 42]. Likewise, SVM, LR, GCN, and monogram outperformed other models in some studies. Several factors influence the predictive performance of ML models, including data size, missing values, feature and engineer selection, ensemble methods, cross-validation, and the expertise of the person conducting the analysis [77; 78]. Thus, it might be difficult to point out which algorithms are more accurate than others, despite the complexity of certain models.