## 3 Experimental Results
### Results of K-TIRADS Classification System  
Random Forest, J48 DT, Ada Boost, and SVM models were employed to test the K-TIRADS system. The findings revealed that SVM yielded the top accuracy rate with 99.5707%. Table 14 shows the success rates for all classifiers. While Table 15 presents the confusion matrix generated for the SVM classifier, Table 16 demonstrates the evaluation metrics for the SVM classifier. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **TP Rate** & **FP Rate** \\ \hline
0 & 0.971 & 0.041 & 0.924 & 0.971 & 0.947 & 0.989 \\ \hline
1 & 0.959 & 0.029 & 0.985 & 0.959 & 0.971 & 0.989 \\ \hline Weighted Avg. & 0.963 & 0.033 & 0.964 & 0.963 & 0.963 & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation metrics for J48 DT. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **FP Rate** & **TP Rate** & **ROC Area** & **Recall** & **F-Measure** & **Precision** \\ \hline
0 & 0.024 & 0.979 & 0.986 & 0.979 & 0.975 & 0.970 \\ \hline
1 & 0.021 & 0.976 & 0.983 & 0.986 & 0.979 & 0.976 \\ \hline Weighted Avg. & 0.022 & 0.977 & 0.977 & 0.986 & 0.977 & 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation metrics for J48 DT. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Accuracy (\%)** \\ \hline Random Forest & 97.4854 \\ \hline J48 DT & **97.7308** \\ \hline Ada Boost & 97.7001 \\ \hline SVM & 93.9589 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative results for the classifiers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Predicted**} \\ \cline{3-6}  & & & **Class “0”** & **Class “1”** \\ \hline
0 & 0.024 & 0.979 & 0.986 & 0.979 & 0.975 & 0.970 \\ \hline
1 & 0.021 & 0.976 & 0.983 & 0.986 & 0.979 & 0.976 \\ \hline Weighted Avg. & 0.022 & 0.977 & 0.977 & 0.986 & 0.977 & 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation metrics for J48 DT. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline
0 & 0.971 & 0.041 & 0.924 & 0.971 & 0.947 & 0.989 \\ \hline
1 & 0.959 & 0.029 & 0.985 & 0.959 & 0.971 & 0.989 \\ \hline Weighted Avg. & 0.963 & 0.033 & 0.964 & 0.963 & 0.963 & 0.989 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation metrics for J48 DT. Although the cross-validation test is a robust and reliable method to evaluate model performance, the external validation test was used to show model generalization capability in this study. To these ends, the EU-ITRADS scoring system, which achieved the best results, was selected for this experiment. In this experiment, 80% of the whole dataset (2609 of 3261) was selected randomly for training the model, and the remaining 20% of the dataset (652 of 3261), which was not used in training process of the model, was selected for testing the model. Random Forest, J48 DT, Ada Boost, and SVM models were used to perform this experiment. The results revealed that Random Forest yielded the top accuracy rate with 98.4663%, as shown in Table 17. While Table 18 presents the confusion matrix generated for the Random Forest classifier, Table 19 demonstrates the evaluation metrics for this classifier. As can be seen from the experimental results, the external validation test results provide proof for the generalization capability of the models and support the validity and reliability of the results obtained with the 10-fold cross-validation test. In addition, these results show that the proposed models can handle the overfitting problem.