## 7 Results
### Results of Machine Learning Models Based on Resampling on Approach 1  
As indicated in Section 7.1, the ML models on the imbalanced CCF dataset do not show good performance in terms of detecting the CCF activities, and thus we need to resample this imbalanced CCF dataset to the balanced CCF dataset, and then apply the ML methods to accomplish better models for CCF detection. According to the resampling approach 1, the performances of seven ML approaches based on SMOTE and ADASYN techniques are evaluated for the prediction of CCF using 30 features, as discussed in Section 3. The confusion matrices of RF, KNN, DT, and LR, and the AdaBoost, XGBoost, and DNN algorithms, based on the resampled CCF dataset, are accomplished to identify the TN, FN, FP, and TP rates. The rates of the machine learning algorithms with SMOTE are illustrated in Table 7. The rates of the machine learning algorithms with ADASYN are represented in Table 8. It is clearly shown that the TP samples are greater than the TN sample based on the ML algorithms on the resampling datasets. This is the first step in demonstrating the effectiveness of applying two resampling techniques to the CCF dataset. Additionally, the fundamental classification performance metrics of seven ML algorithms for CCF detection with SMOTE and ADASYN based on approach 1 are revealed in Figures 12 and 13, respectively. According to Figure 12, the accuracies of the aforementioned algorithms with the resampled CCF datasets are almost similar (above 99%), except the LR algorithm, which has an accuracy of approximately 94.51% based on SMOTE. The precision of the seven ML algorithms also indicates positive outcomes with the highest value of 99.98% for RF with SMOTE. Similarly, the specificity of all of the mentioned algorithms shows very high results (above 99%), except for the LR algorithms, which have a specificity result of approximately 97.51% based on SMOTE. Furthermore, the sensitivity reveals extremely good outcomes, approximately 100% in terms of RF and KNN with the SMOTE technique. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **RF** & **KNN** & **DT** & **LR** & **AdaBoost** & **XGBoost** & **DNN** \\ \hline Accuracy & 99.95\% & 99.94\% & 99.91\% & 99.92\% & 99.92\% & 99.95\% & 99.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracies of the ML models on imbalanced dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RF** & **KNN** & **DT** & **LR** & **AdaBoost** & **XGBoost** & **DNN** \\
**Rate** & **with** & **with** & **with** & **with** & **with** & **with** & **with** \\  & **SMOTE** & **SMOTE** & **SMOTE** & **SMOTE** & **SMOTE** & **SMOTE** & **SMOTE** & **SMOTE** \\ \hline TP & 85,427 & 85,427 & 85,314 & 78,171 & 82,219 & 85,496 & 85,238 \\ FP & 19 & 209 & 270 & 2117 & 1466 & 53 & 262 \\ TN & 85,143 & 84,953 & 84,892 & 83,045 & 83,663 & 85,040 & 85,020 \\ FN & 0 & 0 & 113 & 7256 & 3241 & 0 & 69 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: TP, FP, TN, FN rates of machine learning algorithms with SMOTE based on resampling approach 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **RF** & **KNN** & **DT** & **LR** & **AdaBoost** & **XGBoost** & **DNN** \\
**Rate** & **with** & **with** & **with** & **with** & **with** & **with** & **with** \\  & **ADASYN** & **ADASYN** & **ADASYN** & **ADASYN** & **ADASYN** & **ADASYN** & **ADASYN** \\ \hline TP & 85,428 & 85,429 & 85,366 & 73,864 & 71,308 & 85,205 & 85,276 \\ FP & 30 & 179 & 218 & 7194 & 1475 & 111 & 503 \\ TN & 85,128 & 84,979 & 84,940 & 77,244 & 84,028 & 85,271 & 84,778 \\ FN & 1 & 0 & 63 & 11,565 & 13,776 & 0 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: TP, FP, TN, FN rates of machine learning algorithms with ADASYN based on resampling approach 1. Based on Figure 13, the accuracies of the seven ML classifiers for CCF detection with ADASYN are also very good, except for the LR algorithm, which is approximately 88.96%.