## 4 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metric/Sampling** & **CNN** & **TL** & **All-KNN** & **RENN** & **ENN** & **NCR** & **NM1** & **IHT** \\ \hline
**Accuracy** & 93.65 & 92.59 & 87.30 & 87.30 & 87.30 & 90.47 & 75.66 & 45.50 \\
**Ba Accuracy** & 60.42 & 53.28 & 50.42 & 50.42 & 50.42 & 55.42 & 77.00 & 64.00 \\
**Precision (Mac;Wght)** & 84.52\(\pm\)92.64 & 71.52\(\pm\)98.95 & 50.49\(\pm\)96.40 & 50.49\(\pm\)86.40 & 50.49\(\pm\)86.40 & 59.18\(\pm\)88.30 & 59.07\(\pm\)92.04 & 53.99\(\pm\)90.94 \\
**Recall (Mac;Wght)** & 60.42\(\pm\)93.65 & 53.28\(\pm\)92.59 & 50.42\(\pm\)27.30 & 50.42\(\pm\)27.30 & 50.42\(\pm\)87.30 & 55.42\(\pm\)90.47 & 77.00\(\pm\)75.66 & 64.00\(\pm\)45.50 \\
**MajClass** & 99.43 & 99.43 & 93.71 & 93.71 & 93.71 & 96.57 & 75.43 & 42.29 \\
**MinClass** & 21.43 & 7.14 & 7.14 & 7.14 & 7.14 & 14.29 & **78.57** & **85.71** \\
**F1 Score (Mac;Wght)** & 65.00\(\pm\)91.97 & 54.31\(\pm\)89.93 & 50.43\(\pm\)86.84 & 50.43\(\pm\)86.84 & 50.43\(\pm\)86.84 & 56.56\(\pm\)89.25 & 58.75\(\pm\)81.24 & 38.93\(\pm\)55.99 \\
**ROC\_AUC** & 0.70 & 0.61 & 0.61 & 0.62 & 0.62 & 0.63 & **0.80** & 0.65 \\ \hline \hline \end{tabular} Note: Bolded values indicate significant performance for a metric.  
\end{table} 
Table 4: Performance results of KNN2 (after applying undersampling techniques). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric/Sampling** & **SMOTE** & **SVM SMOTE** & **Borderline SMOTE** & **ADASYN** \\ \hline
**Accuracy** & 88.35 & **91.01** & 90.47 & 87.83 \\
**Ba Accuracy** & 60.85 & 65.57 & 68.57 & 60.57 \\
**Precision (Mac;Wght)** & 59.60\(\pm\)98.09 & 66.67\(\pm\)90.70 & 66.43\(\pm\)91.08 & 58.85\(\pm\)87.83 \\
**Recall (Mac;Wght)** & 60.85\(\pm\)88.35 & 65.57\(\pm\)91.00 & 68.57\(\pm\)90.47 & 60.57\(\pm\)87.83 \\
**MajClass** & 93.14 & 95.43 & 94.29 & 92.57 \\
**MinClass** & 28.57 & 35.71 & **42.86** & 28.57 \\
**F1 Score (Mac;Wght)** & 60.17\(\pm\)88.71 & 66.09\(\pm\)90.85 & 67.41\(\pm\)90.76 & 59.58\(\pm\)88.36 \\
**ROC\_AUC** & 0.72 & 0.74 & 0.74 & 0.72 \\ \hline \hline \end{tabular} Note: Bolded values indicate significant performance for a metric.  
\end{table} 
Table 3: Performance results of KNN2 (after applying oversampling techniques). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metric/Sampling** & **CNN** & **TL** & **All-KNN** & **RENN** & **ENN** & **NCR** & **NM1** & **IHT** \\ \hline
**Accuracy** & 90.47 & 91.53 & 86.24 & 86.24 & 87.30 & 88.35 & 69.84 & 43.91 \\
**Ba Accuracy** & 68.57 & 52.71 & 49.85 & 49.85 & 50.42 & 54.28 & 77.14 & 63.14 \\
**Precision (Mac;Wght)** & 66.43\(\pm\)91.08  
Tables 7 and 8 present the results obtained by RF. Although this algorithm proved effective and suitable for the initial data, after applying both types of sampling, it obtained further superior results. CNN and RENN offer a better balance, achieving high accuracy while maintaining reasonable performance for minority classes. It is also notable that the ADASYN and NM1 techniques achieved values of 85.71%, and 100%, respectively, in the detection of minority classes and ROC_AUC scores of more than 99% with all sampling methods. A smaller area under the ROC curve is visible on the right side of Figure 4, and the left side shows the increase in the area under the ROC curve after applying the oversampling technique technique. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric/Sampling** & **SMOTE** & **SVM SMOTE** & **Borderline SMOTE** & **ADASYN** \\ \hline Accuracy & 86.77 & 88.35 & 86.77 & 85.18 \\ Ba Accuracy & 89.57 & 51.00 & 86.28 & 85.42 \\ Precision (Mac\(\backslash\)Wght) & 67.7294.66 & 50.8986.50 & 66.49/93.92 & 65.12/93.70 \\ Recall (Mac\(\backslash\)Wght) & 89.5786.77 & 51.00/88.35 & 86.28/86.77 & 85.42/85.18 \\ MajClass & 86.86 & 94.29 & 86.86 & 85.14 \\ MinClass & 92.86 & 7.14 & 85.71 & 85.71 \\ F1 Score (Mac\(\backslash\)Wght) & 72.34;89.66 & 50.74/87.15 & 70.69/89.19 & 68.78/88.05 \\ ROC\_AUC & 0.92 & 0.87 & 0.89 & 0.91 \\ \hline \hline \end{tabular}
\end{table}