# Utilization of Machine Learning Algorithms
## 3 Results
### Successes of Machine Learning Interventions in HIV Testing
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Features** & **Successes/Strengths** & **Gaps/Limitations** & **Future Research and Development** \\ \hline \multirow{7}{*}{Study design} & 14 studies utilized primary data, and most of them prospectively predicted HIV testing. This is essential for the following: & Most of the studies employed cross-sectional retrospective designs using secondary data, which are subject to the following: & More studies should evaluate the effectiveness of ML models by using prospective designs \\  & Minimizing missing information Preventing recall bias & Recall bias & Primary studies are essential for capturing real-time information and ensuring high data quality \\  & Capturing important sociodemographic and socio-behavioral data & Imbalanced data & \\ \hline \multirow{7}{*}{Data quality and sources} & \multirow{7}{*}{Somes studies used large samples, providing enough data to train the ML models} & EHRs and hospital records sometimes have limited patient information Many studies used cross-sectional survey data Self-reported information is subject to reveal bias & \multirow{7}{*}{Data used for HIV testing predictive modeling should be cleaned Missing data from datasets should be properly treated The use of primary data should be encouraged for this kind of predictive modeling} \\  & & & & \\ \cline{1-1} \cline{3-4}  & Studies developed and validated models that are highly accurate in predicting HIV testing Studies developed novel models for the first time with high accuracy & Some ML models are complex to interpret false prediction & \multirow{7}{*}{New models need to be trained and validated with different sets of data Training on different sets of models, especially in developing countries, needs to be improved} \\ \hline \multirow{7}{*}{Generalizability} & \multirow{7}{*}{Sutdies evaluated the accuracy of several MLs on a single dataset Studies utilized very large sample sizes} & Many studies applied a single ML algorithm Some samples were very small for predictive modeling Many studies were conducted on a single population/country & \multirow{7}{*}{ML models should be trained using large amounts of data with a variety of variables} \\  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & Some ML models analyzed large amounts of data while addressing data security and privacy concerns with privacy concerns with privacy concerns with privacy concerns with privacy concerns with privacy concerns with HIV testing & \multirow{7}{*}{ML models should be improved, measuring high data protection and privacy-conscious} \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \multirow{7}{*}{Accuracy and performance} & \multirow{7}{*}{ML models are highly accurate and outperformed TMs in several studies ML models accurately classify false-positive/negative results} & \multirow{7}{*}{ML models require more predicting other false positives or false negatives} & \multirow{7}{*}{ML models should be improved, measuring high data protection and privacy-conscious} \\  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \end{tabular} Note: EHRs, electronic health records; HIV, human immunodeficiency virus; HIVST, HIV self-testing; ML, machine learning; TM, traditional methods.  
\end{table} 
Table 6: Successes, gaps, and future directions of machine learning interventions for enhanced HIV testing.