## 3 Results
### Deep Learning  
The versatility of ANNs extends beyond unstructured data, such as images and text, to structured data as well. In our study, we demonstrated the efficacy of ANNs in surpassing traditional ML algorithms when applied to structured data. The model's performance when employed with each of the resampling algorithms and on the raw data is presented in Table 13.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**ANN**} & **Accuracy/** & **Recall/** & **Precision/** & **F1-Score/** & **AUC/** \\  & **Training Accuracy** & **Training Recall** & **Training Precision** & **Training F1-Score** & **Training AUC** \\ \hline Raw data & **0.74/0.73** & **0.78/0.80** & **0.20/0.21** & **0.32/0.33** & **0.76/0.77** \\ SMOTE & 0.58/0.78 & 0.80/0.93 & 0.14/0.71 & 0.23/0.81 & 0.68/0.77 \\ ADASYN & 0.63/0.77 & 0.74/0.92 & 0.15/0.70 & 0.24/0.80 & 0.68/0.76 \\ SMOTE-Tomek & 0.62/0.77 & 0.77/0.94 & 0.15/0.70 & 0.24/0.80 & 0.69/0.77 \\ SMOTE-ENN & **0.54/0.83** & **0.87/0.96** & **0.14/0.79** & **0.24/0.87** & **0.69/0.80** \\ \hline \end{tabular}
\end{table}
Table 13: ANN performance results.  
Figure 6: Confusion matrix for CatBoost and SMOTE-ENN peak performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Model**} & **Accuracy/** & **Recall/** & **Precision/** & **F1-Score/** & **AUC/** \\  & **Training Accuracy** & **Training Recall** & **Training Precision** & **Training F1-Score** & **Training AUC** \\ \hline Logistic Regression & 0.66/0.66 & 0.71/0.73 & 0.15/0.16 & 0.25/0.26 & 0.74/0.75 \\ Decision Tree & 0.79/0.91 & 0.41/0.99 & 0.17/0.48 & 0.24/0.65 & 0.62/0.95 \\ Random Forest & 0.66/0.66 & 0.71/0.73 & 0.15/0.16 & 0.25/0.26 & 0.78/0.98 \\ Gradient Boosting & 0.74/0.74 & 0.67/0.68 & 0.19/0.19 & 0.29/0.30 & 0.78/0.79 \\ XGBoost & 0.61/0.61 & 0.87/0.94 & 0.15/0.17 & 0.26/0.28 & 0.80/0.85 \\ CatBoost & **0.63/0.63** & **0.88/0.94** & **0.16/0.17** & **0.27/0.29** & **0.82/0.86** \\ \hline \end{tabular}
\end{table}
Table 12: Performance results after optimization with SMOTE-ENN.  
The analysis of our results uncovers several significant findings. Firstly, our successful mitigation of overfitting represents a notable achievement, especially when employing an ANN in the context of structured, non-complex data.  
Upon closer inspection, our ANN demonstrates superior performance compared with previously utilized ML models on the raw data. Remarkably, it achieved a recall rate of 78% while matching the accuracy rate attained by the top-performing ML model at 74%. This outcome underscores the effectiveness of our ANN in discerning meaningful patterns within the data.  
Furthermore, the integration of our ANN with the SMOTE-ENN hybrid resampling algorithm yielded outstanding results, culminating in a peak recall rate of 87%. This collaborative approach highlights the efficacy of incorporating data resampling techniques to augment the performance of our ANN.  
While our ANN, in conjunction with SMOTE, ADASYN, and SMOTE-Tomek algorithms, falls slightly short of surpassing our optimized boosting algorithms, it is noteworthy that it achieves a performance closely comparable with them. This suggests that even in scenarios where boosting algorithms maintain a marginal advantage, our ANN remains a competitive and promising alternative.  
The nuanced interplay observed between our ANN and various resampling techniques underscores its adaptability and potential to deliver robust performance across diverse data types.