# Utilization of Machine Learning Algorithms
## 3 Results
### Comparison between Traditional and Machine Learning Predictive Modeling in HIV Testing
#### 3.3.2 Statistical Differences between Traditional and Machine Learning Models  
A total of 43 studies involving the use of either ML or TMs as predictive models for HIV risk/testing were sampled. The results of the independent sample _t_-test indicated a statistically significant difference between the accuracy of ML and TMs (Table 5). On average, the sampled studies from different parts of the world that utilized ML reported a 12.53% (\(p\) = 0.0002) higher accuracy than selected studies involving the use of traditional predictive models. Therefore, our findings indicate that ML models are more accurate than TMs in predicting HIV risk/testing.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Studies** & **Traditional Methods** & **Machine Learning** & **Outcome** \\ \hline [PERSON] et al. [55] & Human interpretation of HIVST & AI algorithm interpretation of HIVST & Humans are more likely to correctly interpret true-positive HIV test results (PPV = 100\%) compared to the AI algorithm (PPV = 82\%) & Humans are more likely to correctly interpret true-positive HIV test results (PPV = 100\%) compared to the AI algorithm (PPV = 82\%) \\  & Human interpretation of HIVST & AI algorithm interpretation of HIVST & The AI algorithm (NPV-100\%) is more likely to correctly interpret true negatives in comparison to humans (NPV-99.9\%) \\ \hline [PERSON] et al. [41] & Human interpretation and empirical models & ML models & The ML model motivated more individuals to conduct HIVST \\  & Human empirical models & ML models & The difference between the ML model and the empirical scale was not significant \\ \hline [PERSON] et al. [39] & Human & ML models & The ML model outperformed human identifications and distribution of HIVST kits \\  & Human & ML models & The ML approach increased HIVST kit distribution by 18\% \\ \hline [PERSON] et al. [32] & TMs & ML models & -The ML models outperformed TMs in HIV risk prediction among MSM -ML achieved 94\% accuracy \\ \hline [PERSON] et al. [63] & TMs & ML & The ML method outperformed TMs The ML method increased the economic benefits of HIVST kit distribution by more than 23\% \\ \hline [PERSON] et al. [34] & TMs & ML & The ML models consistently outperformed the TM used the TM used The ML achieved an accuracy of 76.3\% compared to that of TMs (68\%) \\ \hline [PERSON] et al. [14] & TMs & ML & The TM used outperformed the ML model Both models did not achieve high accuracy \\ \hline [PERSON] et al. [66] & TMs & ML & The ML model outperformed the TM used HIV testing was increased by 18.8\% in the AI group compared to the comparison group (8.1\%) \\ \hline [PERSON] et al. [33] & TMs & ML & ML outperformed TMs ML model achieved an accuracy of 78\% compared to TM (68\%) \\ \hline Conclusion & TMs outperformed ML in only two studies & ML outperformed TMs in seven studies & ML models outperformed TMs based on the empirical evidence from the study’s sample \\ \hline \end{tabular} Note: AI, artificial intelligence; HIV, human immunodeficiency virus; HIVST, HIV self-testing; ML, machine learning; MSM, men who have sex with men; TM, traditional methods; PPV, positive predictive values; NPPV, negative predictive values.  
\end{table}
Table 4: Empirical differences between machine learning and traditional methods in HIV testing predictive modeling.