# Utilization of Machine Learning Algorithms
## 3 Results
### Machine Learning Interventions for Enhanced HIV Testing  
#### 3.2.1 The Machine Learning Algorithms Applied  
The studies included in this review employed various ML algorithms and interventions to enhance HIV testing, along with key findings from the selected studies. Of the 51 articles, 47 utilized several ML types, including supervised and unsupervised learning, and some studies applied both TMs and ML models. Four studies that developed predictive models using only traditional statistical methods were included in the sample. Across all the chosen studies, more than 30 distinct commonly used ML algorithms were implemented. A few studies developed novel ML models such as HealthPulse AI, MySTIRisk, and SexPro for effective HIV risk assessment and improved testing. Most Frequently Utilized Machine Learning Algorithms in HIV Testing Interventions  
The results (Table 2) reveal that the most frequently used ML algorithm was LR, a supervised machine learning (SML) technique applicable for classifications [24], featured in 22 studies with high accuracy [14; 15; 19; 20; 21; 24; 29; 21; 24; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. Random forest, another SML classifier [24], was the second most common algorithm, exhibiting exceptional performance in nearly all the 17 studies in which it was applied [10; 13; 15; 19; 20; 21; 24; 29; 31; 32; 34; 35; 36; 41; 42; 45; 46]. Support vector machine (SVM), an SML algorithm, ranked third and was employed in 14 studies [10; 20; 22; 24; 30; 31; 32; 36; 39; 42; 47; 48]. Figure 5: Study characteristics. PLHIV: people living with HIV; MSM: men who have sex with men. Furthermore, XGBoost was ranked the fourth most common algorithm and was employed in nine studies [10; 13; 23; 24; 29; 30; 34; 35; 40]. Moreover, nine studies employed various deep learning (DL) models, including the artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), and long-short-term memory (LSTM) [13; 24; 30; 31; 48; 49; 50; 51]. The sixth most common algorithm was LASSO, a regression model that combines variable selection and regularization to improve prediction accuracy and interpretability [54]. The LASSO model and decision tree (DT) were each implemented in seven studies, and the K-nearest neighbor (KNN) algorithm appeared in six studies [10; 14; 15; 22; 23; 25; 29; 30; 32; 35; 37; 39; 32; 35; 37; 39; 41; 52; 53]. The least utilized models, including both TMs and ML algorithms, were as follows: gradient boosting machines (GBMs), light gradient boosting machines (LGBMs), elastic net (EN), naive Bayes (NB), principal component analysis (PCA), HealthPulse AI, adapting boosting (Adaboost), extra tree, partial least squares discriminant analysis (PLS-DA), class analogy (SIMCA), MySTIRisk, SexPro, bagging classifier (BC), linear discriminant analysis (LDA), generalized additive model (GAM), classification and regression tree (CART), AI conversational agent, baseline model, unigram model, graph convolutional network (GCN), generalized linear models (GLMs), nomogram, AI Chatbot, association rule algorithm, greedy algorithm, multivariate ML, AI algorithm, Cox proportional hazard regression model, and extreme-gradient-boosted tree model (xgbTree) [10; 15; 17; 20; 21; 22; 24; 29; 30; 31; 34; 35; 37; 40; 42; 46; 50; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67]. The list of all the ML algorithms in the various studies analyzed in this review can be found in the Supplementary Files (Table S6). Best Performing Machine Learning Algorithms  
Several studies utilized multiple algorithms, and their performance metrics are reported in Table 2. XGBoost demonstrated the highest performance compared to other algorithms in [10; 23; 24; 35; 40]. Additionally, RF emerged as the best-performing algorithm compared to other models in the reviewed studies [13; 19; 32; 36]. The DL models, like RNN and CNN models, achieved the best performances in several studies [47; 48; 49].