## 4 Experimental Results and Discussion
### Results
* False Positive (FP): The model incorrectly predicted 4 instances as \"Ex\" that were actually alive. * False Negative (FN): The model incorrectly predicted 1 instance as alive that was actually \"Ex\". * True Negative (TN): The model correctly predicted 3 instances as alive. These numbers give insights into the model's performance in terms of correctly identifying \"Ex\" cases and alive cases. It is important to note that without access to the actual confusion matrices and the specific details of the study, only a general explanation based on the information can be provided. Figures 10 and 11 display the loss and accuracy function graphs for RNN and LSTM, which are DL models used for the \"Ex\" dependent variable with the LDA method. The decreasing loss function indicates a reduction in model error, while the synchronous behavior  
The receiver operating characteristic (ROC) curve is a crucial criterion for evaluating the predictive performance of classification models, including those created using machine learning (ML) and deep learning (DL) techniques. It provides insights into how well the models can make predictions. The ROC curve represents the relationship between recall (true positive rate) and the false positive rate. By graphically displaying this relationship, the ROC curve enables a clear understanding of the trade-off between recall and the false positive rate. In the graph, the false positive rate (FPR) is plotted on the x-axis, while recall (also known as the true positive rate, TPR) is plotted on the y-axis. An ideal ROC curve demonstrates superior performance when the curve approaches the top-left corner of the graph [49]. Figure 12 presents the ROC curves for various machine learning (ML) and deep learning (DL) models. In Figure 12a, LR and RF, as ML models, produced better results. These models have a high true positive rate and a low false positive rate. In Figure 12b, among the DL models, LSTM performed better than RNN. The models created with machine learning (ML) and deep learning (DL) for the dependent variable \"relapse location\" were evaluated using k-fold cross-validation to obtain the mean highest results. Figure 13 presents the confusion matrices for the models generated using the LDA method, which achieved the highest performance. Among the ML models, LR, SVM, RF, DT, and KNN demonstrated similar results and were generally successful in negative classification. Overall, their performance was good. NB showed lower performance compared to the other models. The performance of the LSTM model among the DL models was similar to that of the ML models. RNN achieved the same performance as LSTM. All models had low success in positive classification and low true positive rates. As the relapse location variable consists of three states, the confusion matrix is represented as a 3 \(\times\) 3 matrix. For instance, in the KNN confusion matrix, 16 out of 22 data represent the correct prediction of no tumor recurrence, while 5 data indicate the accurate prediction of tumor presence without recurrence. Additionally, 1 out of 22 data  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]3 RNN accuracy (**a**) and loss (**a**) function graphs of “Ex” dependent variable with LDA. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]4 ROC curve of ML models (**a**) and ROC curve of DL models (**b**) for the “Ex” dependent variable with LDA. represent the correct prediction of relapse by the model. Zeros in the matrix signify cases where the models did not predict the corresponding probabilities. The loss and accuracy function graphs of the DL models, RNN and LSTM, for the relapse dependent variable using the LDA method are shown in Figures 14 and 15. The loss function demonstrates a decreasing trend as the epoch value increases, indicating a reduction in model error. The synchronous accuracy between the training and testing phases indicates that the model effectively predicts while learning.