## 3 Results
### Statistical Tests
#### 3.4.5 Corrected Repeated (Ten-Times) 10-Fold Cross-Validation Paired \(t\)-Test  
Unlike the previous analysis of the corrected 10-fold CV test, this statistical evaluation is based on 100 estimates derived from 10 repetitions of 10-fold CV with different splits, thereby increasing statistical power. The summarized results for the pairwise corrected-repeated 10-fold CV paired \(t\)-test, as proposed by [PERSON] and [PERSON] [21], are presented in Table 10, while the complete statistical data for all pairwise comparisons and metrics (including \(p\)-values and t-scores) are provided in Appendix A (Table A4). As shown in Table 10, the results align closely with those of the previous corrected 10-fold CV test (Table 9), maintaining a consistent overall pattern, particularly in the dominance significance difference count among LR, SVM, and RF model pairs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline
**RF** & & PrC & aprfc & c & & PC & PrC \\ \hline
**ANN** & 3 & & aprfc & pRc & & pc & & \\ \hline
**LR** & 5 & 5 & & APRFC & APRFC & APRFC & APRFC \\ \hline
**SVM** & 1 & 3 & 5 & & rC & PrC & PrC \\ \hline
**LGBM** & & 2 & 5 & 2 & & PC & PC \\ \hline
**CB** & 2 & & 5 & **3** & 2 & & \\ \hline
**XGB** & **3** & & 5 & **3** & **2** & & \\ \hline \hline \end{tabular}  
* A: Accuracy, P: Precision, R: Recall, F: F1-score, C: ROC-AUC. The colors represent the intensity of the statistically significant differences. Dark green indicates differences identified across all performance metrics (regardless of direction), while no color indicates differences identified on one or no metrics. The yellow diagonal separates the lower triangle (number of significant differences) from the upper triangle (metrics where significant differences is identified).  
\end{table} 
Table 10: Aggregate performance of Corrected 10x repeated 10-fold-CV paired \(t\)-test. LR consistently occupied the lowest tier, with highly significant disadvantages (\(p<0.001\)) against all other models on every metric. Its performance gap was particularly evident in accuracy (e.g., LR vs. ANN: t = 6.1035, \(p<0.0001\)) and ROC-AUC (LR vs. CB: t = \(-9.9764\), \(p<0.0001\)). RF did not exhibit significant differences from most models in accuracy or F1. However, it was statistically outperformed in precision by CB (t = \(-3.6917\), \(p=0.0004\)), ANN (t = \(-2.9728\), \(p=0.0037\)), and XGB (t = \(-3.3647\), \(p=0.0011\)). In recall, RF ranked among the top alongside SVM (no significant difference, \(p=0.1702\)), yet in ROC-AUC it was surpassed by CB, XGB, and ANN (all \(p\leq 0.0008\)). SVM demonstrated a pronounced advantage in recall, significantly surpassing CB, XGB, and LGBM (\(p\leq 0.001\)). Its lead over RF was not conclusive (\(p=0.170\)). Nonetheless, for precision and ROC-AUC, SVM was consistently outperformed by ANN, CB, and XGB (\(p<0.001\) in most pairwise comparisons). For the remaining SVM pairwise comparisons across metrics (excluding LR), no statistically significant differences were observed. However, regarding the F1 metric, while no significant differences were detected, SVM exhibited a dominant tendency. This is reflected in its positive t-scores when compared to top-performing models such as CB (t = 0.348, \(p=0.728\)) and XGB (t = 0.490, \(p=0.625\)). ANN ranked among the top three for precision (e.g., ANN vs. SVM: t = 29467, \(p=0.0040\)) and ROC-AUC, where it was statistically indistinguishable from CB and XGB (all \(p>0.0937\)). Meanwhile, it shared no notable differences with RF and LGBM in accuracy or F1, implying a strong yet not dominant position. Regarding RF and SVM, ANN exhibited mixed results, outperforming RF on certain metrics while underperforming on others, such as recall. However, ANN demonstrated a clear advantage over LGBM in cases where statistical differences were identified, including precision (t = 2.151, \(p=0.034\)) and ROC-AUC (t = 2.448, \(p=0.014\)).