# 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
\begin{table}
\begin{tabular}{c c} \hline \hline
**Machine Learning Interventions** & **Key Findings** & **References** \\ \hline \multirow{8}{*}{*} & Models were developed; ML models that accurately predicted individuals at the highest risk of HIV for prioritized testing & \\  & were validated & -HIV diagnosis was accurately predicted from secondary data & \\  & A study predicted monthly incidences of HIV from 2020 to 2030 & \\  & Biomarker indicators for HIV diagnosis were identified & \\  & Dynamic changes in the immune state were associated with HIV & \\  & Age, gender, race, wealth index, province, contraceptive use, sexual & \\ \multirow{8}{*}{*} & Predicting HIV risk/testing behavior, condom use, alcohol/drug usage, knowledge of HIV, intimate & \\  & Socio-behavioral and demographic predictors of HIV & partner violence, hepatitis diagnosis, pregnancy, depression, male & \\  & precidents of HIV & \\  & Studies developed models such as MySTIRisk and SexPro for self-HIV & \\  & risk assessment & \\  & -The number of those who needed to be tested to find one undiagnosed & \\  & PLHIV from certain communities/countries was predicted & \\  & HIV diagnosis from magnetic resonance imaging (MRI) was predicted & \\  & using ML & \\  & -HIV prevalence and incidence was forecasted & \\ \hline \multirow{8}{*}{*} & Models that enhance early HIV diagnosis using multiple ML from & \\  & secondary data were developed & \\  & Models predicted the number of people to be diagnosed with HIV in the & \\  & next decade by analyzing their socio-behavioral data & \\  & Individuals’ HIV statuses were predicted using DHS, EHRs, and & \\  & hospital records & \\  & HIV hotspots in multiple countries were identified & \\  & ML models improved workflow with the ability to report immediately to & \\  & reduce infection & \\ \hline \multirow{8}{*}{*} & ML is more likely to correctly interpret true-negative HIV test results & \\  & (NPV = 100\%) & \\  & ML models improve diagnostics performance and reduce & \\  & false-positive/negative results & \\  & Healthful Pulse AI provides accurate and consistent results in interpreting & \\  & HIV RDT test results & \\ \multirow{8}{*}{*} & Accurate/Efficient HIV testing & ML models that are efficient and effective for HIV testing among KPs & \\  & Cost and resource optimization in & such as MSM were developed & \\  & HIV testing & \\ \multirow{8}{*}{*} & Misinterpretation of HIV test results & ML methods outperformed traditional approaches in several studies & \\  & Studies developed ML algorithms that can accurately facilitate HIV & \\  & counseling and testing as well as interpret HIV test results with and & \\  & without internet connectivity & \\  & Cost-effective HIV testing for low-income countries was achieved & \\  & An ML method increased the economic benefits of HIVST kit & \\ \hline \multirow{8}{*}{*} & Enhancing HIV-Self Testing & ML models enhance SHIVT and improve interpretations of results & \\  & Innovative approaches in HIV & Chatbots and conversational agents with mHealth solutions improve & \\  & diagnostic tools & convenience for using HIVST & \\ \cline{1-1} \cline{2-4}  & & Models diagnosed HIV from MRI using ML & \\ \hline \hline \end{tabular} Note: HIV, human immunodeficiency virus; HIVST, HIV self-testing; ML, machine learning; MRI, magnetic resonance imaging; DHS, demographic and health survey; EHRs, electronic health records; MSM, men who have sex with men; STI, sexually transmitted infections.  
\end{table} 
Table 3: Machine learning interventions employed to enhance HIV testing. A nested case-control study in Peru utilized LASSO ML to predict HIV acquisition, incorporating selected biomarkers and socio-behavioral covariates among MSM and transgender women [52]. The findings underscored that changes in immune states are associated with HIV acquisition, and the inclusion of biomarker, demographic, and behavioral data provided complementary information to assess HIV risk [52].