# Utilization of Machine Learning Algorithms
## 3 Results
### Machine Learning Interventions for Enhanced HIV Testing
#### 3.2.2 Machine Learning Interventions Employed to Enhance HIV Testing  
In this review, studies were given priority based on their focus on ML interventions within specific thematic areas. These thematic areas include the prediction of HIV risk/test/status, evaluation of the accuracy and efficiency of ML in HIV testing compared \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**ML Algorithms** & **No. of Studies** & \begin{tabular}{l} **Largest Dataset** \\ **Size/Reference** \\ \end{tabular} & **Best Performance/Reference** & **Binned Size** & **References** \\ \hline LR & 19 & 4,348,178 [15] & \begin{tabular}{l} 90.5\% highest accuracy \\ Best model in [14; 36] \\ \end{tabular} & Large & [14; 15; 19; 20; 21; 24; 29; 30; 31; 32; 33; 34; 35; 36; 41; 42; 37; 43; 44] \\ \hline RF & 15 & 4,348,178 [15] & \begin{tabular}{l} 94.4\% highest accuracy \\ Best model in [13; 19; 32; 34; 36] \\ \end{tabular} & Large & [10; 13; 15; 19; 20; 21; 24; 29; 31; 32; 33; 34; 36; 41; 42; 45; 46] \\ \hline SVM & 13 & 124,777 [40] & \begin{tabular}{l} 95.1\% highest accuracy \\ Best model in [22] \\ \end{tabular} & Large & [10; 20,22; 24; 30; 32; 36; 39; 42; 47; 48] \\ \hline XGBoost & 9 & 124,777 [40] & \begin{tabular}{l} 90.5\% highest sensitivity \\ Best model in [10; 23; 24; 35; 40] \\ \end{tabular} & Large & [10; 13; 23; 24; 29; 30; 34; 35; 40] \\ \hline DL models (ANN, RNN, CNN, LSTM) & 9 & 88,642 [13] & \
\begin{tabular}{l} 90.8\% highest accuracy \\ Best model in [47; 48; 49] \\ \end{tabular} & Large & [13; 24; 30; 31; 47; 48; 49; 50; 51] \\ \hline LASSO model & 7 & 4,348,178 [15] & 82\% highest accuracy & Large & [15; 22; 24; 37; 52; 53] \\ \hline DT & 7 & 56,682 [25] &
\begin{tabular}{l} 81.9\% highest accuracy \\ Best model in [31] \\ \end{tabular} & Large & [14; 25; 31; 32; 35; 39; 50] \\ \hline KNN & 6 & 6672 [35] & 80\% highest accuracy & Small & [10; 24; 29; 30; 31; 35] \\ \hline \hline \end{tabular}  
* Note: LR, logistic regression; RF, random forest; SVM, support vector machine; XGBoost, extreme gradient boosting; DL, deep learning; ANN, artificial neural network; RNN, recurrent neural network; CNN, convolutional neural network; LSTM, long-short-term memory; LASSO, least absolute shrinkage and selection operator; DT, decision tree; KNN, K-nearest neighbor.  
\end{table} 
Table 2: Most frequently utilized machine learning algorithms in HIV testing interventions. to TMs, and novel ML techniques for enhanced HIV testing uptake, including HIVST. Furthermore, several studies investigated and identified predictors related to HIV risk, status, and testing. The ML interventions are elaborated in the following thematic contexts:  
* Predicting HIV risk/testing: As summarized in Table 3, numerous studies predicted individuals at the highest risk of HIV, as well as those who were likely to undergo HIV testing. This study's central focus on predictive modeling within the context of HIV testing has unearthed compelling evidence showcasing the effectiveness of ML in enhancing HIV testing interventions. For instance, [15] utilized LASSO models, LR, and RF to predict the HIV status of individuals based on nationwide electronic data. The findings demonstrated enhanced predictive performance, estimating that 384 individuals would need to be tested to identify one undiagnosed person with HIV [15]. A parallel study by [51] used DL models to forecast HIV incidence between 2022 and 2023 in the Philippines. The study predicted a cumulative case count of 145,273 by 2030 [51].