## 3 Results
Subsequently, the classification results of these features between the Control and Cisplatin  
Figure 5: Classification groups. groups were evaluated. As shown in Table 1, the DT classification achieved an accuracy of 73% \(\pm\) 0.06 and an F-score of 75% \(\pm\) 0.03 for the Control and Cisplatin groups. In contrast, the KNN, ANN, and SVM methods achieved accuracy values close to 91.33%. The results of the Control and CisExo groups are analyzed in Table 3. After analyzing the KNN classification results, an F-score of 89% \(\pm\) 0.04 and an accuracy of 90% \(\pm\) 0.03 were achieved. In the SVM results, an accuracy of 86% \(\pm\) 0.07 and an F-score of 88% \(\pm\) 0.06 were obtained. For the DT classification, an accuracy of 76% \(\pm\) 0.12 and an F-score of 77% \(\pm\) 0.08 \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Accuracy** & **CorrecRate** & **Sensitivity** & **Specificity** & **Precision** & **NPV** & **F-Score** \\ \hline DT & 0.73 \(\pm\) 0.06 & 0.75 \(\pm\) 0.02 & 0.74 \(\pm\) 0.05 & 0.76 \(\pm\) 0.04 & 0.77 \(\pm\) 0.03 & 0.73 \(\pm\) 0.03 & 0.75 \(\pm\) 0.03 \\ \hline KNN & 0.92 \(\pm\) 0.02 & 0.92 \(\pm\) 0.02 & 0.90 \(\pm\) 0.02 & 0.95 \(\pm\) 0.03 & 0.95 \(\pm\) 0.03 & 0.90 \(\pm\) 0.01 & 0.92 \(\pm\) 0.02 \\ \hline SVM & 0.90 \(\pm\) 0.02 & 0.87 \(\pm\) 0.01 & 0.81 \(\pm\) 0.02 & 0.93 \(\pm\) 0.04 & 0.93 \(\pm\) 0.04 & 0.82 \(\pm\) 0.01 & 0.86 \(\pm\) 0.01 \\ \hline ANN & 0.92 \(\pm\) 0.05 & 0.86 \(\pm\) 0.05 & 0.81 \(\pm\) 0.04 & 0.92 \(\pm\) 0.1 & 0.92 \(\pm\) 0.09 & 0.82 \(\pm\) 0.04 & 0.86 \(\pm\) 0.05 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance metrics for classification between Control and Cisplatin groups. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Accuracy** & **CorrecRate** & **Sensitivity** & **Specificity** & **Precision** & **NPV** & **F-Score** \\ \hline DT & 0.67 \(\pm\) 0.04 & 0.73 \(\pm\) 0.03 & 0.78 \(\pm\) 0.03 & 0.67 \(\pm\) 0.05 & 0.72 \(\pm\) 0.03 & 0.74 \(\pm\) 0.04 & 0.75 \(\pm\) 0.03 \\ \hline KNN & 0.70 \(\pm\) 0.04 & 0.67 \(\pm\) 0.01 & 0.60 \(\pm\) 0.09 & 0.7 were recorded, while in ANN, an accuracy of 88% \(\pm\) 0.05 and an F-score of 89% \(\pm\) 0.05 were achieved. The results for the Control and ExoCis groups are shown in Table 4. According to the evaluation results, the accuracy for SVM, ANN, and KNN classification is approximately 74.66%, while the DT classification yields an accuracy of 61% \(\pm\) 0.02. The performance of each classification algorithm varies among the different groups. For instance, in the Control-Cis group, the KNN and DT algorithms exhibited higher accuracy rates compared to other models. However, SVM and ANN models demonstrated a wider range of accuracy values, occasionally yielding lower accuracy scores. This suggests that these models may be more affected by variances within this group. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Accuracy** & **CorrecRate** & **Sensitivity** & **Specificity** & **Precision** & **NPV** & **F-Score** \\ \hline DT & 0.61 \(\pm\) 0.02 & 0.70 \(\pm\) 0.05 & 0.62 \(\pm\) 0.00 & 0.79 \(\pm\) 0.10 & 0.74 \(\pm\) 0.09 & 0.69 \(\pm\) 0.03 & 0.67 \(\pm\) 0.04 \\ \hline KNN & 0.75 \(\pm\) 0.04 & 0.70 \(\pm\) 0.06 & 0.82 \(\pm\) 0.07 & 0.60 \(\pm\) 0.13 & 0.66 \(\pm\) 0.07 & 0.78 \(\pm\) 0.06 & 0.73 \(\pm\) 0.04 \\ \hline SVM & 0.75 \(\pm\) 0.05 & 0.75 \(\pm\) 0.06 & 0.89 \(\pm\) 0.04 & 0.61 \(\pm\) 0.14 & 0.69 \(\pm\) 0.09 & 0.86 \(\pm\) 0.04 & 0.78 \(\pm\) 0.04 \\ \hline ANN & 0.74 \(\pm\) 0.06 & 0.68 \(\pm\) 0.02 & 0.71 \(\pm\) 0.08 & 0.66 \(\pm\) 0.09 & 0.66 \(\pm\) 0.04 & 0.71 \(\pm\) 0.03 & 0.68 \(\pm\) 0.03 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance metrics for classification between Control and ExoCis groups. Figure 7: Accuracy results of five different groups’ classification performance. In the Control-CisExo group, the DT model achieved the highest accuracy. Similarly, the KNN and ANN models also produced high accuracy rates, while the SVM model's accuracy was generally lower.