## 3 Results
### Smote-Enn  
In the concluding phase, we implemented the SMOTE-ENN hybrid resampling algorithm on the BRFSS data, merging synthetic data generation with data refinement strategies. This approach strategically increased the minority class to 193,714 instances, substantially reinforcing its presence in the dataset. Concurrently, it pruned the majority class, reducing its observations to 133,109 from the original count of 198,647.  
The performance results after applying the SMOTE-ENN algorithm are presented in Table 11.  
Upon scrutinizing these findings, a notable enhancement was discernible across all models concerning recall, while maintaining a commendable accuracy rate, subsequent to the implementation of the SMOTE-ENN technique. For example, gradient boosting demonstrated a substantial surge from 4% to 67% without any additional optimization. Furthermore, it is noteworthy that both logistic regression and random forest achieved an impressive 79% recall. Moreover, with our dataset now balanced, we could explore the AUC metric, revealing that nearly all our models exhibited commendable proficiency in correctly classifying instances, with random forest notably achieving an impressive 80%.  
Moving forward to optimize the models, we meticulously selected hyperparameters tailored to our data, prioritizing those assigning different weights to each class. This strategic decision emphasizes the minority class, which holds particular significance in our context. The results, as illustrated in Table 12, are promising. Interestingly, we mitigated overfitting across all models, as evidenced by the consistent performance on both the training and test sets. Additionally, we achieved peak performance for each model. Particularly, CatBoost surpassed expectations with a remarkable 88% recall, alongside a decent accuracy rate and an impressive 82% AUC rate.  
\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 & 0.76/0.76 & 0.49/0.50 & 0.16/0.17 & 0.25/0.25 & 0.72/0.73 \\ Decision Tree & 0.86/0.98 & 0.21/0.79 & 0.20/0.93 & 0.20/0.85 & 0.59/1.00 \\ Random Forest & 0.76/0.76 & 0.49/0.50 & 0.16/0.17 & 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 & **0.67/0.68** & **0.82/0.89** & **0.17/0.19** & **0.28/0.31** & **0.80/0.85** \\ CatBoost & 0.70/0.71 & 0.81/0.90 & 0.18/0.21 & 0.30/0.34 & 0.81/0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance results after optimization with SMOTE-Tomek.  
\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 & **0.60/0.61** & **0.79/0.80** & **0.14/0.15** & **0.24/0.25** & **0.75/0.76** \\ Decision Tree & 0.79/0.91 & 0.42/1.00 & 0.17/0.48 & 0.24/0.65 & 0.62/0.95 \\ Random Forest & 0.60/0.61 & 0.79/0.80 & 0.14/0.48 & 0.24/0.25 & 0.79/0.99 \\ Gradient Boosting & 0.74/0.74 & 0.67/0.68 & 0.19/0.19 & 0.29/0.30 & 0.78/0.79 \\ XGBoost & 0.85/0.86 & 0.42/0.50 & 0.25/0.30 & 0.31/0.38 & 0.80/0.84 \\ CatBoost & 0.87/0.89 & 0.37/0.48 & 0.28/0.36 & 0.32/0.41 & 0.81/0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance results after implementing SMOTE-ENN.  
This outcome highlights the effectiveness of integrating CatBoost with the hybrid SMOTE-ENN algorithm, particularly for healthcare professionals grappling with imbalanced real-world datasets.  
As depicted in Figure 6, this combination proves highly advantageous, facilitating the detection of a significant number of positive cases--an invaluable asset for the healthcare sector by identifying 6548 out of the total 7477 individuals at risk.