## 4 Experimental Results and Discussion
### Results
* RNN and LSTM models obtained 69% F1-score. * RNN and LSTM models achieved 79% accuracy. * LSTM model achieved 99% AUC score. These models showed higher performance compared to other models in terms of the mentioned performance metrics. Lastly, the results obtained from the models created using the LDA method for the \"tumor recurrence\" dependent variable are provided in Table 4. Based on the given information, models created without applying any feature extraction techniques generally exhibited low performance. LSTM demonstrated the highest performance across all metrics and is the strongest model. LSTM achieved performance values ranging between 79 and 99% in terms of precision, recall, F1-score, and accuracy. RNN stands out as the second-best model after LSTM, showing particularly good performance with AUC values of 97-99% in PCA and LDA methods. The LDA method improved performance in models such as LR, RF, and DT. While PCA provided performance improvements in some models, it was ineffective in others. Overall, KNN and SVM showed lower performance compared to other models. According to the provided information, Table 5 presents the results obtained from ML and DL models for the dependent variable \"cause of death location\" before applying any feature extraction technique. Based on these results:  
* RF model achieved 85% precision. * LR, DT, and RF models achieved 86% recall. * RF model obtained 85% F1-score. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{**Precision (\%)**} & \multicolumn{3}{c}{**Recall (\%)**} & \multicolumn{3}{c}{**F1-Score (\%)**} & \multicolumn{3}{c}{**Accuracy (\%)**} & \multicolumn{3}{c}{**AUC (\%)**} \\ \hline  & **N-M** & **PCA** & **LDA** & **N-M** & **PCA** & **LDA** & **N-M** & **PCA** & **LDA** & **N-M** & **PCA** & **LDA** & **N-M** & **PCA** & **LDA** \\ \hline LR & 46 & 46 & 76 & 68 & 68 & 73 & 55 & 55 & 65 & 31 & 68 & 73 & 91 & 91 & 52 \\ \hline KNN & 46 & 46 & 46 & 68 & 68 & 68 & 55 & 55 & 55 & 68 & 68 & 6  
DT and RF models achieved 86% accuracy. * LSTM model achieved an AUC score of 96%. These models demonstrated higher performance compared to other models in terms of the mentioned performance metrics for the \"cause of death location\" dependent variable. Furthermore, Table 5 also includes the results obtained from models created using the PCA method for the \"cause of death location\" dependent variable. Based on these results:
* LR model achieved 80% precision. * LR, DT, RF, RNN, and LSTM models achieved 82% recall. * LR model achieved 81% F1-score. * SVM, DT, RF, RNN, and LSTM models achieved 82% accuracy. * LSTM model achieved an AUC score of 98%. These models showed higher performance compared to other models in terms of the mentioned performance metrics. Lastly, the results obtained from the models created using the LDA method for the \"cause of death location\" dependent variable are provided in \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]0 However, the specific performance metrics and values for these models are not mentioned in the information provided. According to the provided information, LSTM demonstrated the highest performance across all metrics and is the best model. It achieved a precision performance between 67 and 85%, while recall and F1-score ranged between 82 and 85%. Under the LDA method, the LSTM model showed the best performance with an AUC value of 99%. RNN achieved results close to LSTM in terms of AUC and accuracy. Overall, KNN and SVM showed lower performance compared to other models. For instance, the AUC value of KNN remained between 76 and 80% across all methods. The information provided emphasizes the utilization of the k-fold cross-validation method in the model to address errors that may arise due to variations between the training and testing datasets. This technique involves randomly partitioning the data into k subsets, where in each subset is used for both training and testing in an iterative manner.