## 4 Experimental Results and Discussion
### Results
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]5 LSTM loss (**a**) and accuracy (**b**) function graphs of the relapse location dependent variable with LDA. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]6 Confusion matrix of ML and DL models of relapse site dependent variable with LDA. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]7 RNN loss (**a**) and accuracy (**b**) function graphs of the relapse location dependent variable with LDA. Figure 16 shows the ROC curve of different ML and DL algorithms for the relapse location dependent variable. As seen in Figure 16, the position of the Logistic Regression model at the top of the ROC curve indicates a high AUC value and a high level of classification accuracy. LR outperforms all other models. While KNN and SVM models show good performance, Gaussian Naive Bayes, Decision Tree Classifier (DTC), and Random Forest models exhibit lower performance. The LSTM model outperforms the RNN model in terms of performance. Furthermore, the ROC curve of the LSTM model demonstrates a notably low false positive rate and a high true positive rate. The models created with machine learning (ML) and deep learning (DL) for the dependent variable \"tumor recurrence\" achieved their highest performance through k-fold cross-validation. Figure 17 presents the confusion matrices of the models generated using the LDA method. Among the ML models, LR, SVM, RF, DT, and KNN demonstrated similar results but failed in positive classification. Overall, their performance was good. NB showed better performance in the positive class compared to the other models. The performance of the LSTM model among the DL models was similar to that of NB. RNN failed in the positive class. As an example, the SVM confusion matrix demonstrates that out of the 22 data points, 15 were correctly classified as the absence of tumors by the model, and no tumors were found by the model. Additionally, 1 data point represents the accurate prediction of tumor presence without recurrence, while 6 data points represent the correct prediction of tumor relapse by the model. Zeros in the matrix indicate cases where the models did not predict the corresponding probabilities. Figure 18 shows the ROC curve of different ML and DL algorithms for the tumor recurrence dependent variable. The Logistic Regression, SVM, and Random Forest models are the models with the highest performance. These models have a high true positive rate and a low false positive rate. KNN, Gaussian Naive Bayes, and Decision Trees are closer to a flat line, indicating lower performance. Models that make random predictions have low classification success rates. The LSTM model outperforms the RNN model. While the ROC curve of the LSTM model demonstrates ideal performance in classification problems, the performance of the RNN model is quite limited. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]8 ROC curve of ML models (**a**) and ROC curve of DL models (**b**) for the relapse site dependent variable with LDA. The models created with machine learning (ML) and deep learning (DL) achieved their highest performance using k-fold cross-validation. Figure 19 presents the confusion matrices of the models generated using the LDA method. Among the ML models, LR, SVM, RF, NB, and KNN demonstrated similar results and showed good performance in both positive and negative classes. DT, however, has a higher number of false positives compared to other ML models. Among the DL models, the LSTM model exhibited the best performance. RNN failed in the positive class and was the weakest model. For instance, in the LSTM confusion matrix, out of the 22 datasets, 18 were correctly classified by the model as cardiac arrest when the cause of death was indeed cardiac arrest. Additionally, three data points were correctly identified by the model as deaths caused by organ failure, although the cause of death was labeled as multi-organ failure. Moreover, there was one data point representing cases where no cause of death was found, and the model predicted the same.