# 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
Similarly, several SML algorithms were applied to the Zimbabwe DHS dataset to predict HIV positivity [35]. The study highlighted that condomless sex, multiple sex partners, a lack of understanding about HIV transmission, being unemployed, and intimate partner abuse were all related to HIV transmission [35]. From a retrospective analysis of EHRs, the XGBoost and LASSO models were able to accurately predict the incidences of HIV diagnoses among women in the USA [23]. Various studies have also used EHRs, DHS, and clinical data to predict HIV risk/testing based on sociodemographic and socioeconomic variables in both general and KPs [10; 13; 14; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 36; 38; 40; 47; 48; 50; 53; 58; 59]. Machine learning can predict those at higher risk of HIV infection, improve optimal HIV screening choices, and facilitate HIV testing [69]. Our analysis identified socio-demographic, socio-behavioral, and other factors associated with increased HIV risk for prioritized testing. A study by the authors of [52] discovered that dynamic changes in immune states are associated with HIV acquisition, and biomarker, demographic, and behavioral data added complementary information to HIV risk. Studies from the USA highlight factors such as old age, inpatient visits, alcohol use, male gender, condom use, sexual behavior, and substance use as key predictors of HIV risk and diagnosis [19; 20; 21; 22]. Furthermore, studies on MSM in China and Australia used different algorithms to predict HIV risk and timely HIV testing [24; 32]. Triage reasons such as STI symptoms, asymptomatic screening, sex overseas in the previous 12 months, PLHIV, contact with someone with the infection, current sex work, screening reminder frequency, type of screening reminder method, sex with a male in the previous three months, and condom use were among the major predictors [24; 32]. Most of the studies from SSA were conducted in East and Southern Africa, including the Population-Based HIV Impact Assessment (PHIA) countries. The findings of [10; 14; 31; 40; 42; 58] showed that age, relationship with the family head, the highest level of education, the highest grade at that school level, work for payment, avoiding pregnancy, age at the first experience of sex, wealth quintile, male circumcision, sex, province, sexual debut, sexually transmitted infection (STI), race, wealth index, contraceptive use, longitude, latitude, altitude, knowledge of HIV, place of residence, age of the most recent partner, total lifetime number of sexual partners, years lived in the current place of residence, having receptive vaginal sex, and condom use during the last intercourse were significant predictors of HIV status. In the In the Mano River Union countries (Liberia, Sierra Leone, Cote d'Ivoire, and Guinea), Cote d'Ivoire was reported as the hotspot for HIV prevalence between 2005 and 2015, while Guinea and Sierra Leone became hotspots for HIV prevalence from 2016 to 2020 [53]. In addition, the study indicated that HIV prevalence was higher among women than among men who lived in urban areas. The model used in the study further predicted that comprehensive correct knowledge about HIV was below the UNAIDS threshold for ending HIV endemic [53]. * Accurate and efficient HIV testing interventions utilizing machine learning techniques: This review encompasses studies that employed novel technologies and algorithms to enhance HIV testing accuracy and efficiency. In a study conducted in France, [PERSON] et al. [25] employed DL techniques to enhance the efficiency of HIV diagnostic tests. The research involved a retrospective analysis of \"Centaur(r) CHIV\" assays and confirmatory tests conducted at Amiens University Medical Center between 2012 and 2018 [25]. The findings revealed the detection of 38% false positives and 62% confirmed true positives [25]. Remarkably, the models demonstrated high accuracy by significantly reducing the number of false-positive CHIV assay results from 171 to 12 [25].