## 4 Results and Analysis
### Prediction Results
#### 4.3.1 Prediction Results Based on Imbalanced Training Dataset
The _acc_, _AUC_, _F1_, _PRE_, and _REC_ of XGBoost reach 0.908 \(\pm\) 0.0420 (the best of three ensemble classifiers), 0.950 \(\pm\) 0.0550 (second only to the RF classifier at 0.956 \(\pm\) 0.0483), 0.938 \(\pm\) 0.0240 (the best of the three ensemble models), 0.922 \(\pm\) 0.0522 (the best of the three ensemble models, equal to the mean of the GBDT classifier, with a standard deviation less than the GBDT classifier), and 0.957 \(\pm\) 0.0383 (second only to the RF classifier at 0.967 \(\pm\) 0.0479), respectively. * The performance of SVM, KNN, LR, and DT classifiers are relatively poorer than that of the stacking ensemble classifier and tree-based classifiers XGBoost, RF, and GBDT. For _acc_, DT has the highest accuracy at 0.872 \(\pm\) 0.0374, followed by SVM at 0.871 \(\pm\) 0.0474, and KNN and LR have the worst accuracy at 0.858 \(\pm\) 0.0384 and 0.858 \(\pm\) 0.0385, respectively. For _AUC_, SVM has the highest value of 0.945 \(\pm\) 0.0417; LR takes second place, with a value of 0.932 \(\pm\) 0.0774; the next is DT, which is 0.890 \(\pm\) 0.0958; and KNN is the worst, with a value of 0.897 \(\pm\) 0.0916. For _F1_, all four individual classifiers are greater than 0.9 and relatively close. The _F1_ scores of SVM, KNN, LRm and DT are 0.917 \(\pm\) 0.0230, 0.910 \(\pm\) 0.0174, 0.912 \(\pm\) 0.0228, and 0.911 \(\pm\) 0.0173, respectively. For _PRE_, only DT is greater than 0.9, at 0.922 \(\pm\) 0.0509. Furthermore, the PRE values of the other three classifiers are all below 0.9, and the PRE values of SVM, KNN, and LR are 0.868 \(\pm\) 0.0512, 0.842 \(\pm\) 0.0314, and 0.839 \(\pm\) 0.0324, respectively. For _REC_, all four individual classifiers are greater than 0.9. The _REC_ values for SVM, KNN, DT, and LR are 0.975 \(\pm\) 0.0273, 0.991 \(\pm\) 0.0149, 0.901 \(\pm\) 0.0605, and 0.997 \(\pm\) 0.0072, respectively. This phenomenon is also due to the imbalanced dataset and will be discussed in Section 4.3.2. * For the _kappa_ coefficient, the stacking ensemble classifier is the highest, at 0.784 \(\pm\) 0.1437. The three tree class ensemble classifiers XGBoost, RF, and GBDT take second place, with 0.743 \(\pm\) 0.1479, 0.722 \(\pm\) 0.1420, and 0.710 \(\pm\) 0.1450, respectively. The four individual classifiers, SVM, KNN, LR, and DT have relatively low mean values below 0.7, which are 0.597 \(\pm\) 0.2372, 0.555 \(\pm\) 0.2070, 0.667 \(\pm\) 0.1148, and 0.547 \(\pm\) 0.2085, respectively. This phenomenon reflects the advantage of ensemble classifiers in the processing imbalanced dataset. In conclusion, it can be seen that the stacking ensemble classifier has the best performance. The _acc_, _AUC_, _F1_, _PRE_, and _kappa_ values of the stacking ensemble classifier are the highest among all classifiers. At the same time, the _PRE_ and _REC_ values of the stacking ensemble classifier are relatively balanced, which indicates that the stacking ensemble classifier has strong adaptability to imbalanced datasets. For XGBoost, RF, and GBDT, the results are second only to the stacking ensemble classifier, and their \(PRE\) and \(REC\) values are both greater than 0.9, indicating that the ensemble classifiers have strong adaptability to imbalanced datasets. For SVM, KNN, LR, and DT, their performances are relatively poor. With the exception of DT, there is a significant gap between the PRE and REC values of the other three individual classifiers. Although cost-sensitive methods were used for the three individual classifiers, the results were still poor.