# Utilization of Machine Learning Algorithms
## 4 Discussion
### Successes and Opportunities of Machine Learning Interventions in HIV Testing  
This study demonstrated that algorithms can effectively predict individuals at the highest risk of HIV as well as crucial related parameters to determine the priority population for HIV testing. Our findings indicate that ML methods outperformed human efforts and traditional statistical predictive models utilized across countries to improve HIV testing. The independent \(t\)-test from our study shows that ML models achieved 12.53% (\(p\) = 0.002) higher accuracy compared to TMs. These findings are consistent with those of [PERSON] et al. [6], who reviewed different ML models and concluded that they were more effective in HIV risk prediction than traditional approaches. Several studies forecast the number of people who will be HIV-positive in the following decade [15]. In addition, socio-behavioral data have been used to predict HIV incidence and prevalence in the Philippines and a few African countries [51; 53]. This is critical for the HIV testing cascade because it enables relevant stakeholders to identify which groups of people are most risk of contracting HIV and to plan targeted interventions for efficient resource allocation [10]. Findings from a review of AI applications in HIV prevention by [PERSON] et al. [69] align with ours. Moreover, the study uncovers the power of ML in predicting which socio-demographic and socio-cultural factors are associated with HIV testing. In the USA, old age, inpatient visits, alcohol use, male gender, condom use, sexual behavior, and substance use were identified as key predictors of HIV risk and diagnosis [19; 20; 21; 22]. Similar to the finding of a study that looked at gender differences as predictors of sexual behaviors and HIV testing in Kenya, young men were found to be more likely to engage in risky sexual behavior [79]. Despite this, males are more reluctant to undertake HIV testing due to barriers such as knowledge deficits, stigma, and perceived HIV risk [80; 81]. However, [PERSON] et al. [53] reveal that HIV prevalence is higher in women in the Mano River countries. Our results are consistent with those of a review of ML in HIV prevention by the authors of [69], whose findings indicate that ML has the potential to identify individuals at the highest risk of HIV for the early initiation of treatment. In addition, KPs such as MSM are one of society's most vulnerable groups. Even in areas with a supportive legal framework, they face societal stigma. This impairs their willingness to seek medical attention, exposing them and their partners to HIV risk. This study demonstrates the predictive power of ML interventions for HIV risk, testing, and status. Asymptomatic screening of STI symptoms, sex overseas in the previous 12 months, persons living with HIV, contact with infection, current sex work, screening reminder frequency, the type of screening reminder method, sex with a male in the previous three months, and condom use were among the major predictors of HIV risk, testing, and status among MSM, according to studies from Australia and China [24; 32]. This review affirms that ML interventions can enhance accurate and efficient HIV testing. They are not only capable of predicting the prevalence of HIV testing, but are also applicable in diagnostic devices to detect false-positive HIV test results and improve the privacy, accessibility, and convenience of HIV testing. With the aid of DL in France, 38% of false-positive HIV results were detected from a retrospective analysis [25]. Likewise, findings from the USA detected a significant number of false-positive HIV test results usingML models [22; 57]. This represents a significant milestone in pursuing the 95-95-95 goal, as the use of ML prevents testers from experiencing conflicting emotions and strengthens their confidence in healthcare providers. Machine learning has also been very effective in reducing false-negative HIV test results, which are more concerning because falsely diagnosed PLHIV can increase the spread of HIV [48; 55].