# Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
## 5 Results
### Ablation Experiment  
This section discusses the verification of the proposed method's generalization ability. The experiment involved training models on a balanced dataset that was created using the proposed resampling algorithm. After training, classification tasks were performed on the test dataset. The confusion matrix of the XGB model on the balanced dataset that was created using the proposed resampling algorithm is shown in Figure 5. Table 5 presents the the performance evaluation indicators of the classification results for various models. XGB has the highest indicators with an accuracy of 80.52%, precision of 81.85%, recall of 80.40%, F1 score of 81.08%, and AUC value of 85.98%. The model improves accuracy by 0.8%, recall by 2.62%, F1 score by 0.55%, and AUC by 1.57% compared to the imbalanced data. Although precision indicators decreased, the overall performance improved. Furthermore, the results show that other classification models also achieved better performance after using the proposed resampling algorithm, with most indicators improving. This indicates that the proposed resampling algorithm has addressed the issue of bias in the model caused by an imbalanced dataset. T-SNE (t-distributed stochastic neighbor embedding) was used to visualize high-dimensional data in a three-dimensional space. The scatter plot in the three-dimensional space represents each class with a different color and pattern. Figure 6 shows imbalanced samples, where classes Ah and Aga have fewer instances than the other classes. Figure 7 shows that the instances of the four classes are almost the same after balancing.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline
**Proposed method** & **80.52** & **82.62** & **81.96** & **81.08** & **85.98** \\ SMOTEENN & 77.31 & 72.88 & 80.40 & 76.04 & 84.27 \\ SMOTETomek & 78.57 & 80.94 & 79.31 & 80.00 & 84.99 \\ SMOTE & 79.61 & 82.48 & 79.67 & 80.98 & 85.39 \\ BorderSMOTE & 79.72 & 81.85 & 80.23 & 81.03 & 84.68 \\ RandomOver & 78.57 & 81.99 & 79.05 & 80.44 & 84.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of the different resampling methods with the XGB model.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline
**Proposed** & **80.87** & **84.02** & **82.53** & **82.57** & **87.10** \\ XGB & 80.52 & 81.85 & 80.40 & 81.08 & 85.98 \\ SVM & 68.49 & 57.11 & 63.76 & 58.45 & 75.69 \\ KNN & 56.24 & 46.59 & 54.04 & 45.50 & 69.34 \\ GB & 7  
After conducting experiments on two different datasets, the XGB model was found to perform the best. To address the issue of data imbalance, the bagging method was employed to combine different models with the proposed resampling algorithm. Tables 5 and 6 show the values of different performance measures for the different ensemble models. The results indicate that XGB with bagging achieved the highest AUC of 87.10% and had enhanced recall and F1 scores of 82.53% and 82.57%, respectively. The final confusion matrix of the model is shown in Figure 5. The bagging method improved the performance of different models slightly, demonstrating its effectiveness compared to a single classifier. Additionally, the bagging method alleviates the problem of an imbalanced dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline
**XGB** & **80.87** & **82.80** & **82.53** & **82.57** & **87.10** \\ SVM & 70.67 & 57.29 & 59.83 & 58.04 & 74.00 \\ KNN & 57.27 & 46.74 & 54.64 & 46.61 & 69.68 \\ GB & 78.57 & 81.16 & 80.11 & 80.58 & 85.39 \\ DT & 71.48 & 66.46 & 73.71 & 69.28 & 80.92 \\ MLP & 71.13 & 62.40 & 69.05 & 64.74 & 78.64 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performances of the different classifiers when using the bagging method.  
Figure 6: Distribution of imbalanced gout dataset.  
Figure 7: Distribution of gout dataset after resampling.