## 3 Results
### Meta-Analysis
#### 3.7.2 Subgroup Analyses  
To better understand and explain the high heterogeneity, additional subgroup analyses based on SSA subregions were conducted to explore potential sources of variability. A comparison of the AI/ML approaches' performance according to SSA subregions was carried out by conducting a clustering analysis. The clustering analysis of regions based on F1-scores and mean sample sizes reveals a significant variation in model performance and data availability across SSA. The cluster plot in Figure 10 shows that the AI/ML approaches' performance can be subdivided into three clusters. Southern Africa, represented by Cluster 2, demonstrates the highest F1-scores and the largest mean sample sizes, indicating robust model performance and substantial data availability. East Southern Africa, part of Cluster 1 along with West Africa, is characterized by large sample sizes but relatively low F1-scores,  
Figure 9: Forest plot [39; 42; 44; 45; 48; 52; 54].  
suggesting that, despite substantial data resources, model performance in this region is limited. West Africa, also in Cluster 1, exhibits both small sample sizes and low F1-scores, implying challenges in both data availability and model accuracy. East Africa, included in Cluster 3, shows moderate F1-scores and sample sizes, reflecting balanced but less optimal performance compared to Southern Africa. These findings highlight Southern Africa as the best-performing region, with East Southern Africa's performance limited by low F1-scores, despite large samples, emphasizing the need for improved data collection and model performance in regions like West Africa.  
A comprehensive and robust subgroup analysis according to regions was conducted through the Kruskal-Wallis test and ANOVA, see Table 8. This was important to leverage the complementary strengths of these tests. The Kruskal-Wallis test helped to handle skewed data with outliers and violations of normality assumptions for nonparametric analysis. The ANOVA helped with addressing normality and homogeneity assumptions for parametric analysis. The researcher adopted both tests to allow for cross-validation of findings for credible and valid conclusions. The Kruskal-Wallis test for F1-scores yielded a test statistic H = 6.5859 with a \(p\)-value of 0.0862, indicating insufficient evidence at the 5% significance level to reject the null hypothesis. This suggests that there are no significant differences among the F1-scores across the regions of SSA. However, the ANOVA for F1-scores yielded a \(p\)-value of 0.0228, providing significant evidence of meaningful differences among the regions. Post hoc analysis using [PERSON]'s test further identified a significant difference in F1-scores, specifically between Southern Africa and East Southern Africa (\(p\) = 0.0314), aligning with the ANOVA results. Thus, while most SSA subregions show no major differences in F1-scores, significant differences exist between Southern Africa and East Southern Africa. Southern Africa consistently showed stronger performance in F1-scores compared to East Southern Africa, implying the need for further investigation into the specific factors contributing to these differences. The Kruskal-Wallis test and ANOVA for sample sizes yielded \(p\)-values = 0.000, supporting significant evidence of differences in sample sizes among the regions in SSA, and this aligned with the post hoc analysis  
Figure 10: Cluster plot.  
using [PERSON]'s test, which showed sample size differences between East Southern Africa and Southern Africa, East Africa and West Africa, East Southern Africa and West Africa, Southern Africa and West Africa, East Africa and East Southern Africa, and East Africa and Southern Africa. This confirms strong variability in sample sizes across the SSA regions. The correlation between F1-scores and sample sizes yielded r = 0.0055 with a \(p\)-value of 0.9643, suggesting a lack of evidence to support a linear relationship between F1-scores and sample sizes.