## 4 Experimental Results and Discussion
### Results
Furthermore, the results obtained from the models created using the PCA method for the \"Ex\" dependent variable are provided in Table 2. Among these results, the DT model demonstrated higher performance with 92% precision, 91% recall, 90% F1-score, 90% accuracy and LSTM achieved an AUC value of %97 compared to other models. Lastly, the results obtained from the models created using the LDA method for the \"Ex\" dependent variable are also shown in Table 2. LR, KNN, SVM, and NB achieved 86% precision, LR, KNN, SVM, NB, RNN, and LSTM achieved 82% recall, LR, KNN, SVM, and NB achieved 83% F1-score, LR, KNN, and SVM achieved 82% accuracy and LSTM achieved an AUC value of 98%. The aforementioned models demonstrated superior performance when compared to the other models evaluated in the study. The inclusion of precise specifications and particulars pertaining to the models, encompassing their configurations, quantity, and dataset employed, remains absent from the accessible information. Nevertheless, the available information does proffer valuable observations regarding performance metrics, such as precision, recall, F1-score, and accuracy, concerning the \"Ex\" dependent variable. This permits a comparative analysis of the models within diverse scenarios. According to the information provided, Table 3 presents the results obtained from ML and DL models for the dependent variable \"relapse location\" before applying any feature extraction technique. Based on these results:  
* DT model achieved 81% precision. * RNN and LSTM models achieved 82% recall. * RNN and LSTM models obtained 74% F1-score. * RNN and LSTM models achieved 82% accuracy. * LSTM model achieved 99% AUC score. These models demonstrated higher performance compared to other models in terms of the mentioned performance metrics for the \"relapse location\" dependent variable. Furthermore, Table 3 also includes the results obtained from models created using the PCA method for the \"relapse location\" dependent variable. Based on these results:  
* RNN and LSTM models achieved 67% precision. * RNN and LSTM models achieved 82% recall. * RNN and LSTM models obtained 74% F1-score. * 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 \"relapse location\" dependent variable are provided in Table 3. However, the specific performance metrics and values for these models are not mentioned in the information provided. \begin{table}
\begin{tabular}{c 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 & 82 & 85 & 86 & 82 & 86 & 82 & 82 & 86 & 83 & 86 & 86 & 82 & 66 & 70 & 80 \\ \hline KNN & 67 & 75 & 86 & 82 & 86 & 82 & 74 & 7  
According to the information provided, Table 4 presents the results obtained from ML and DL models for the dependent variable \"tumor recurrence\" before applying any feature extraction technique. Based on these results:  
* RNN and LSTM models achieved 62% precision. * RNN and LSTM models achieved 79% recall. * RNN and LSTM models obtained 69% F1-score. * RNN and LSTM models achieved 79% accuracy. * The LSTM model achieved an AUC score of 97%. These models demonstrated higher performance compared to other models in terms of the mentioned performance metrics for the \"tumor recurrence\" dependent variable. Additionally, Table 4 also includes the results obtained from models created using the PCA method for the \"tumor recurrence\" dependent variable. Based on these results:  
* RNN and LSTM models achieved 62% precision. * RNN and LSTM models achieved 79% recall.