## 3 Results
### Machine Learning Models
XGBoost exceeds CatBoost in terms of precision, specificity, and misclassification rate, whereas CatBoost outperforms XGBoost in terms of accuracy, \(F1\)-score, and AUC. The other machine learning models do not exhibit a consistent pattern in performance ranking. When AUC was used to compare LightGBM to Random Forest, CatBoost, and XGBoost, the differences were 0.005, 0.010, and 0.011, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **\(F1\)-Score** & **Misclassification Rate** & **AUC** \\ \hline Logistic Regression & 0.7302 & 0.7581 & 0.6763 & 0.7842 & 0.7148 & 0.2698 & 0.7302 \\ LogitBoost & 0.7518 & 0.7869 & 0.6906 & 0.8129 & 0.7356 & 0.2482 & 0.7518 \\ MLP & 0.7446 & 0.7361 & 0.7626 & 0.7266 & 0.7491 & 0.2554 & 0.7446 \\ AdaBoost & 0.7986 & 0.8374 & 0.7410 & 0.8561 & 0.7863 & 0.2014 & 0.7986 \\ CatBoost & 0.8381 & 0.8357 & 0.8417 & 0.8345 & 0.8387 & 0.1619 & 0.8381 \\ LightGBM & 0.8165 & 0.8548 & 0.7626 & 0.8705 & 0.8061 & 0.1835 & 0.8165 \\ XGBoost & 0.8165 & 0.8235 & 0.8058 & 0.8273 & 0.8145 & 0.1835 & 0.8165 \\ Gradient Boosting & 0.8273 & 0.8760 & 0.7626 & 0.8921 & 0.8154 & 0.1727 & 0.8273 \\ kNN & 0.8201 & 0.7764 & 0.8993 & 0.7410 & 0.8333 & 0.1799 & 0.8201 \\ Naive Bayes & 0.6942 & 0.7647 & 0.5612 & 0.8273 & 0.6473 & 0.3058 & 0.6942 \\ SVM & 0.7518 & 0.7917 & 0.6835 & 0.8201 & 0.7336 & 0.2482 & 0.7518 \\ Decision Tree & 0.7266 & 0.7203 & 0.7410 & 0.7122 & 0.7305 & 0.2734 & 0.7266 \\ Random Forest & 0.8273 & 0.8421 & 0.8058 & 0.8489 & 0.8235 & 0.1727 & 0.8273 \\ \hline \end{tabular}
\end{table} 
Table 7: Performances of the model during training. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **\(F1\)-Score** & **Misclassification Rate** & **AUC** \\ \hline Logistic Regression & 0.6748 & 0.4700 & 0.6351 & 0.6919 & 0.5402 & 0.3252 & 0.6635 \\ LogitBoost & 0.6707 & 0.4646 & 0.6216 & 0.6919 & 0.5318 & 0.3293 & 0.6567 \\ MLP & 0.6870 & 0.4835 & 0.5946 & 0.7267 & 0.5333 & 0.3130 & 0.6607 \\ AdaBoost & 0.7114 & 0.5190 & 0.5541 & 0.7791 & 0.5359 & 0.2886 & 0.6666 \\ CatBoost & 0.7295 & 0.5316 & 0.5676 & 0.7849 & 0.5490 & 0.2805 & 0.6762 \\ LightGBM & 0.7439 & 0.5797 & 0.5405 & 0.8314 & 0.5594 & 0.2561 & 0.6860 \\ XGBoost & 0.7236 & 0.5395 & 0.5541 & 0.7965 & 0.5467 & 0.2764 & 0.6753 \\ Gradient Boosting & 0.7114 & 0.5185 & 0.5676 & 0.7733 & 0.5419 & 0.2886 & 0.6704 \\ kNN & 0.6870 & 0.4842 & 0.6216 & 0.7151 & 0.5444 & 0.3130 & 0.6684 \\ Naive Bayes & 0.6992 & 0.5000 & 0.5811 & 0.7500 & 0.5375 & 0.3008 & 0.6655 \\ SVM & 0.6585 & 0.4468 & 0.5676 & 0.6977 & 0.5000 & 0.3415 & 0.6326 \\ Decision Tree & 0.5976 & 0.3786 & 0.5270 & 0.6279 & 0.4407 & 0.4024 & 0.5775 \\ Random Forest & 0.7317 & 0.5541 & 0.5541 & 0.8081 & 0.5541 & 0.2683 & 0.6811 \\ \hline \end{tabular}
\end{table} 
Table 8: Performances of the models using the optimum hyperparameters obtained. Figure 4: Bar graphs showing the performances of the trained models for the different performance measures: (**a**) accuracy, (**b**) precision, (**c**) sensitivity, (**d**) specificity, (**e**) \(F1\)-score, (**f**) misclassification rate, and (**g**) AUC.