## 7 Results
### Results of Machine Learning Models Based on Resampling on Approach 1
This is similar to the case of SMOTE, and the values of other metrics, such as precision, sensitivity, and specificity of the ML algorithms with ADASYN, show very positive outcomes. However, the sensitivity results of LR and AdaBoost based on ADASYN are quite low compared with other classifiers, at only approximately 86.46% and 83.81%, respectively. In general, we also can observe that the results of the seven ML methods for CCF detection based on SMOTE and ADASYN are quite similar in each ML approach. The fundamental metrics illustrate excellent outcomes of the seven ML approaches with the resampling CCF dataset. The fundamental classification metrics show the results of not only the accuracy value, but also other values, as mentioned, to evaluate the ML models of CCF detection more comprehensively. However, the performance of LR and AdaBoost classifiers for CCF detection based on both SMOTE ADASYN have the lowest results regarding all fundamental metrics among seven classifiers. Figure 12: Fundamental classification performance measurements of ML algorithms with SMOTE based on resampling approach 1. Figure 13: Fundamental classification performance measurements of ML algorithms with ADASYN based on resampling approach 1. Next, in terms of combined metrics based on the resampling approach 1, indexes, such as the F1 score, G-mean, balanced accuracy, and MCC, of the seven ML algorithms for CCF detection which relied on SMOTE and ADASYN are displayed in Figures 14 and 15, respectively. Overall, the results of the combined metrics of the ML models based on resampling CCF dataset are also very high. The results of RF, KNN, DT, XGBoost, and DNN classifiers for CCF with SMOTE and ADASYN are also similar, above 99%. However, the outcomes of LR and AdaBoost with SMOTE and ADASYN show relatively low results, particularly the result of only 77.23% of the MCC index in the case of ADASYN. We see that LR with ADASYN has the lowest results of all indexes in the combined metrics. However, RF and DNN with SMOTE and ADASYN results are almost similar and show the highest values for all these indexes to detect CCF. Based on the resampling approach 1, the positive likelihood ratios of the seven ML models of CCF detection that were computed are shown in Table 9. Based on Table 3, it is clear that all of the model contributions are good. However, there is a dramatic difference between the outcomes of these models. The results of RF are very high, at about 4482.21, while LR shows very low results, at only 10.15. Finally, we also debate graphical assessment performance in order to make a sufficient evaluation of the CCF activities in this study. The comparisons of AUC of ML algorithms with SMOTE and ADSYN based on the resampling approach 1 are also described in Figures 16 and 17, respectively. Based on Figure 16, the AUC of RF and KNN based on the SMOTE technique shows the highest results, at about 100%, whereas the AUC value of AdaBoost is relatively low, at about 97.24%. Additionally, as shown in Figure 17, the AUC results of seven ML algorithms with ADASYN also show the same trend as SMOTE, with approximately 100% of RF and KNN. This is followed by XGBoost, DT, and DNN, at approximately 99.93%, 99.8%, and 99.38%, respectively. AdaBoost also indicates the lowest value, at around 91.04%.