## 3 Results  
### Model Performance Evaluation  
The types of hyperparameters and their parameter spaces identified during hyperparameter optimization are illustrated in Table 3. Table 4 provides insights into the VRM performance using the AUC-ROC, accuracy, precision, recall, and the F1-score. The performance of the five machine learning models was evaluated using the mean values of the performance metrics with their respective 95% confidence interval (CI) values. As shown in Table 4, the SVM and the RF reported the highest AUC-ROC scores of 0.745 and 0.746, with CIs of \(\pm\)0.001 and \(\pm\)0.002, respectively. Both k-NN and XGBoost demonstrated comparable AUC-ROC scores, with k-NN achieving 0.739 and XGBoost achieving 0.740, each with a confidence interval (CI) of \(\pm\)0.002. In contrast, the decision tree (DT) model exhibited the lowest AUC-ROC value among the machine learning models, scoring 0.731 with a CI of \(\pm\)0.002. The highest model accuracy of 0.753 was demonstrated by the RF, with a CI of \(\pm\)0.002, followed by the SVM at 0.749, XGBoost at 0.746, and k-NN at 0.743. In contrast, the lowest accuracy of 0.736 was reported by the DT with a CI of 0.003. The precision metric revealed that the RF and XGBoost reported the highest precision scores of 0.671 and 0.661, respectively. k-NN and the SVM showed similar recall scores of 0.726 and 0.727. The RF exhibited the third highest recall value of 0.718, followed by XGBoost at 0.711 and the DT at 0.707. The highest F1-score was achieved by the RF, reaching 0.693 (SE). The lowest F1-scores were shown by k-NN at 0.668 and the DT at 0.664. The developed ranking scheme reported that the RF was the best model, followed by the SVM, XGBoost, and k-NN. The DT was reported as the least suited model (Table 5). The means of the performance metrics and the confidence interval (CI) values of each machine learning method were used to generate the ranking system. For the AUC-ROC, accuracy, precision, and the F1-score, the RF was found to be the best machine learning algorithm. However, the RF and the SVM ranked first according to the AUC-ROC, while the RF and XGBoost were found to be the top models for the precision metric. The models that performed the best for the recall values were the SVM and k-NN, with the RF coming \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ML** & **AUC-ROC** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline DT & 0.731 \(\pm\) 0.002 & 0.736 \(\pm\) 0.003 & 0.629 \(\pm\) 0.02 & 0.707 \(\pm\) 0.006 & 0.664 \(\pm\) 0.009 \\ k-NN & 0.739 \(\pm\) 0.002 & 0.743 \(\pm\) 0.002 & 0.618 \(\pm\) 0.003 & 0.726 \(\pm\) 0.003 & 0.668 \(\pm\) 0.002 \\ SVM & 0.745 \(\pm\) 0.001 & 0.749 \(\pm\) 0.01 & 0.641 \(\pm\) 0.002 & 0.727 \(\pm\) 0.003 & 0.681 \(\pm\) 0.002 \\ RF & 0.746 \(\pm\) 0.002 & 0.753 \(\pm\) 0.002 & 0.671 \(\pm\) 0.003 & 0.718 \(\pm\) 0.003 & 0.693 \(\pm\) 0.002 \\ XGBoost & 0.740 \(\pm\) 0.002 & 0.746 \(\pm\) 0.002 & 0.661 \(\pm\) 0.007 & 0.711 \(\pm\) 0.003 & 0.685 \(\pm\) 0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance metric evaluation (mean values of each metric with 95% confidence intervals for various machine learning algorithms). \begin{table}
\begin{tabular}{c c} \hline \hline
**ML Algorithm (Classifier)** & **Identified Hyperparameter Features** \\ \hline \multirow{2}{*}{DT} & min\_samples\_split = 10, min\_samples\_leaf = 3, max\_features = ‘log2’, max\_depth = 5, \\  & criterion = ‘entropy’ \\ \hline k-NN & n\_neighbors = 151 \\ \hline SVM & kernel = ‘rbf’, C = 18 \\ \hline XGBoost & colsample\_bytree = 0.7, learning\_rate = 0.01, max\_depth = 15, subsample= 0.9, \\  & n\_estimators = 1300 \\ \hline RF & n\_estimators = 1200, min\_samples\_split = 4, min\_samples\_leaf = 10, \\  & max\_features= ‘sqrt’, max\_depth = 10, criterion = ‘gini’ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Identified hyperparameters and their search spaces used for five different machine learning algorithms. in third and XGBoost and the DT coming in fourth.