## 3 Results
### SMOTE-Tomek  
Subsequently, we implemented the SMOTE-Tomek algorithm, a hybrid resampling technique widely adopted for addressing data imbalance. Through this approach, the minority class experiences augmentation, reaching 198,031 instances, significantly strengthening its presence within the dataset. Concurrently, the Tomek links algorithm identifies and eliminates instances forming Tomek links--pairs of instances from different classes that are nearest to each other--facilitating a targeted reduction in the majority class.  
Following the resampling process, we applied our ML models, and the ensuing results are presented in Table 9.  
With our data now balanced, we find satisfaction in the 91% accuracy achieved by the boosting algorithms. However, the 8% recall rate reveals a notable drawback, as the models tend to favor the majority class, thus hindering their ability to accurately detect positive cases. Consequently, we find the performance of logistic regression and random forest more appealing as they strike a balance between relatively high accuracy and a satisfactory level of recall.  
Upon fine-tuning the models, as depicted in Table 10, we observed that decision trees unfortunately overfit to the training data, while logistic regression and random forest experienced a drop in performance. Interestingly, CatBoost and XGBoost demonstrated noteworthy performance improvements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy/** & **Recall/** & **Precision/** & **F1-score/** & **AUC/** \\  & **Training Accuracy** & **Training Recall** & **Training Precision** & **Training F1-score** & **Training AUC** \\ \hline Logistic Regression & 0.75/0.76 & 0.50/0.50 & 0.16/0.16 & 0.25/0.25 & 0.71/0.72 \\ Decision Tree & 0.85/0.98 & 0.23/0.78 & 0.18/0.88 & 0.20/0.82 & 0.59/1.00 \\ Random Forest & 0.74/0.76 & 0.56/0.50 & 0.16/0.16 & 0.25/0.25 & 0.78/1.00 \\ Gradient Boosting & 0.83/0.90 & 0.41/0.17 & 0.21/0.27 & 0.27/0.21 & 0.77/0.79 \\ XGBoost & **0.67/0.68** & **0.81/0.89** & **0.17/0.18** & **0.28/0.31** & **0.80/0.85** \\ CatBoost & 0.70/0.71 & 0.80/0.90 & 0.18/0.20 & 0.30/0.34 & 0.81/0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance results after optimization (ADASYN).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy/** & **Recall/** & **Precision/** & **F1-Score/** & **AUC/** \\  & **Training Accuracy** & **Training Recall** & **Training Precision** & **Training F1-Score** & **Training AUC** \\ \hline Logistic Regression & **0.70/0.70** & **0.63/0.61** & **0.16/0.16** & **0.25/0.25** & **0.74/0.74** \\ Decision Tree & 0.84/1.00 & 0.27/1.00 & 0.17/1.00 & 0.21/1.00 & 0.58/1.00 \\ Random Forest & 0.70/0.70 & 0.63/0.61 & 0.16/0.16 & 0.25/0.25 & 0.78/1.00 \\ Gradient Boosting & 0.83/0.84 & 0.40/0.40 & 0.21/0.22 & 0.28/0.28 & 0.77/0.78 \\ XGBoost & 0.90/0.91 & 0.12/0.16 & 0.31/0.43 & 0.17/0.24 & 0.80/0.84 \\ CatBoost & 0.91/0.92 & 0.08/0.15 & 0.40/0.63 & 0.14/0.24 & 0.81/0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance results after implementing SMOTE-Tomek.  
CatBoost not only achieved an impressive 70% accuracy but also excelled in identifying positive cases, boasting an 81% recall rate. Similarly, XGBoost outperformed all models with an 82% sensitivity rate, marking the highest result thus far. These outcomes underscore the effectiveness and robustness of boosting algorithms in navigating the intricacies of the dataset.