## 2 Materials and Methods
### Statistical Analysis  
Multiple evaluation techniques were employed to address the acknowledged absence of a single definitive approach for determining which machine learning model performs best and by what margin [19]. The selection of statistical tests was guided by prior research, highlighting the need to minimize both Type I (false positive) and Type II (false negative) errors in comparative analyses. As a result, a suite of advanced statistical methods--namely, the 5 \(\times\) 2 cross-validation paired \(t\)-test, the 10-fold cross-validation paired \(t\)-test, the [PERSON] non-parametric pairwise signed-rank test, the corrected random resampled \(t\)-test, the corrected k-fold cross-validation \(t\)-test, the corrected repeated k-fold cross-validation \(t\)-test, [PERSON]'s test, and the non-parametric Friedman test--was incorporated to evaluate performance across metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation remains one of the most prevalent methods for model evaluation, particularly in cases with limited data availability [28]. However, K-fold cross-validation is known to exhibit high variability, which can lead to erratic behavior in estimated prediction errors and potentially misleading outcomes during model selection [18]. Moreover, there is no unbiased estimator of the variance in K-fold cross-validation, nor is there conclusivetheoretical support indicating that any existing bias becomes negligible when the sample size is not asymptotically large [19]. In light of these concerns, multiple evaluation techniques--along with the correction strategies proposed by [PERSON] and [PERSON] [20] and by [PERSON] and [PERSON] [21]--were adopted to mitigate the impact of correlation within training and test folds [35]. Such an integrative approach was deemed critical to reducing the risk of misleading conclusions by leveraging complementary insights from distinct evaluation procedures. Consequently, a multifaceted approach was deemed necessary to mitigate these biases and ensure that performance differences among models could be more reliably detected. The \(5\times 2\) cross-validation paired \(t\)-test was chosen due to its relatively low risk of Type I error, as suggested by empirical studies [18]. Although it does not fully eliminate the correlation introduced by reusing training and test splits within the same procedure, its iterative structure--consisting of five repetitions of twofold splits--offers a practical balance between computational feasibility and variance control. Nevertheless, generating only 10 metric points in this design can yield insufficient statistical power, as preliminary power analyses indicated that approximately 100 measures would be optimal, thereby suggesting that the Type II error rate may be appreciably elevated. Consequently, the \(5\times 2\) cross-validation \(t\)-test should be interpreted with caution, particularly regarding its ability to detect subtle yet potentially meaningful performance differences. The 10-fold cross-validation paired \(t\)-test, by contrast, is associated with a higher Type I error rate but offers greater sensitivity when detecting performance differences. Notwithstanding, this approach also tends to produce a relatively limited number of aggregated measurements, which can reduce its overall statistical power. Despite these constraints, it was included in the present study owing to its frequent use in the literature, where it is often preferred for balancing computational efficiency and the need for comparative performance assessment. The Wilcoxon non-parametric pairwise signed-rank test was additionally employed to address potential violations of normality assumptions. This test was performed on the performance estimates derived from the 10-fold cross-validation procedure and from ten times repeated 10-fold cross-validation (100 aggregate measurements).