## 3 Results and Discussion  
The performances of six classification models--DT, NB, SVM, k-NN, RF, and DL--were evaluated using several key metrics. The metric results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Model** & **DT** & **NB** & **SVM** & **k-NN** & **RF** & **DL** \\ \hline Accuracy & 0.85 & 0.77 & 0.86 & 0.87 & 0.89 & 0.85 \\ \hline
95\% CI & (0.82, 0.87) & (0.74, 0.81) & (0.82, 0.88) & (0.84, 0.90) & (0.86, 0.90) & (0.82, 0.87) \\ \hline NIR \({}^{*}\) & 0.76 & 0.76 & 0.76 & 0.76 & 0.76 & 0.76 & 0.76 & 0.76 \\ \hline _p_-value [Acc > NIR] & 4.14 \(\times\) 10\({}^{-8}\) & 0.22 & 0.00 & 2.12 \(\times\) 10\({}^{-12}\) & 5.59 \(\times\) 10\({}^{-16}\) & 7.25 \(\times\) 10\({}^{-8}\) \\ \hline Kappa & 0.63 & 0.19 & 0.66 & 0.6807 & 0.7153 & 0.6215 \\ \hline McNemar’s Test _p_-value & 0.0006 & \(<\)2 \(\times\) 10\({}^{-16}\) & 3.583 \(\times\) 10\({}^{-10}\) & 0.0007 & 0.0051 & & 0.0025 \\ \hline Classes (Cl) & Cl:1 & Cl:2 & Cl:3 & Cl:1 & Cl:2 & Cl:3 & Cl:1 & Cl:2 & Cl:3 & Cl:1 & Cl:2 & Cl:3 \\ \hline Sensitivity & 0.42 & 0.72 & 0.90 & 0.29 & 0.05 & 0.99 & 0.58 & 0.84 & 0.87 & 0.63 & 0.76 & 0.91 & 0.46 & 0.82 & 0.92 & 0.46 & 0.73 & 0.89 \\ \hline Specificity & 0.94 & 0.93 & 0.86 & 0.97 & 1.00 & 0.21 & 0.92 & 0.92 & 0.94 & 0.95 & 0.94 & 0.88 & 0.96 & 0.94 & 0.90 & 0.94 & 0.92 & 0.84 \\ \hline Pos Pred Value & 0.20 & 0.71 & 0.95 & 0.25 & 0.75 & 0.80 & 0.23 & 0.73 & 0.98 & 0.32 & 0.75 & 0.96 & 0.29 & 0.77 & 0.97 & 0.23 & 0.71 & 0.95 \\ \hline Neg Pred Value & 0.98 & 0.93 & 0.74 & 0.97 & 0.80 & 0.89 & 0.98 & 0.96 & 0.69 & 0.98 & 0.94 & 0.76 & 0.98 & 0.95 & 0.79 & 0.98 & 0.93 & 0.71 \\ \hline Prevalence & 0.04 & 0.20 & 0.76 & 0.04 & 0.20 & 0.76 & 0.04 & 0.20 & 0.76 & 0.04 & 0.20 & 0.76 & 0.04 & 0.20 & 0.76 & 0.04 & 0.2 & 0.76 \\ \hline Detection Prevalence & 0.08 & 0.20 & 0.72 & 0.04 & 0.01 & 0.94 & 0.09 & 0.23 & 0.67 & 0.07 & 0.21 & 0.72 & 0.06 & 0.22 &The RF model achieved the highest accuracy at 89% (95% CI: 86-90%), followed closely by k-NN (87%) and SVM (86%). DL and DT yielded comparable performance (85% and 85%, respectively), while NB had the lowest accuracy at 77%. Comparisons against the No Information Rate (NIR = 76%) showed statistically significant improvement for all models (\(p<0.001\)) except NB (\(p\) = 0.22), indicating that NB did not significantly outperform random classification. Kappa statistics, indicating agreement beyond chance, were highest for RF (0.72); moderate for k-NN (0.68), SVM (0.66), DT (0.63), and DL (0.62); and poor for NB (0.19). The Sensitivity and Specificity varied across classes (Cl:1, Cl:2, and Cl:3). For Cl:3 (the most prevalent class, 76%), all models showed high Sensitivity (>87%) and strong Specificity (mostly above 0.85), with RF and k-NN performing particularly well with Cl:3 sensitivities of 0.92 and 0.91, respectively. However, the Sensitivity for the minority classes (Cl:1 and Cl:2) was considerably lower, especially for NB (Cl:1 = 0.29; Cl:2 = 0.05) although RF and DL offered better performances across these classes. Balanced Accuracy, which accounts for imbalanced class distributions, was highest for DL (Cl:1: 0.82, Cl:2: 0.86, and Cl:3: 0.92) and RF (Cl:1: 0.71, Cl:2: 0.88, and Cl:3: 0.91), highlighting their robustness across classes. The F1 Scores, representing a balance between Precision and Recall, were highest in RF (0.35-0.95 across classes) and DL (0.41-0.94), emphasising their ability to maintain high predictive performance even on minority classes. [PERSON]'s Test \(p\)-values further suggested that for most models, there were significant disagreements between predicted and true classes (\(p<0.05\)), indicating room for improvement in model consistency, particularly for NB (\(p<2\times 10^{-16}\)) and SVM (\(p=3.58\times 10^{-10}\)). Overall, RF provided the best overall balance between accuracy, class-wise performance, and reliability. K-NN was a close second, especially effective in handling class imbalance and delivering high accuracy and F1 Scores across all classes.