## 4 Experimental Results and Discussion
### Results  
In this study, machine learning (ML) and deep learning (DL) models were trained and tested, and their performance was evaluated using various performance metrics. The evaluation of model performance involved multiple metrics to ensure a comprehensive assessment, as relying solely on a single metric may not be sufficient for datasets that are not evenly distributed. The performance metrics used in this study included precision (Precision-PR), recall (Recall-RE), F1-score (F1-SC), accuracy (Accuracy-ACC), and Receiver Operating Characteristic (ROC) analysis. These metrics provide valuable insights into different aspects of model performance. For binary classification problems, the 2 \(\times\) 2 confusion matrix is commonly used to visualize the predictions made by the models. The matrix displays the number of samples predicted for each of the four possible outcomes: true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). These values form the basis for deriving various classifier performance metrics. In the context of liver transplantation, F1-SC is more meaningful as it represents the overall performance of the model in terms of minimizing both false positives and false negatives. The application of AI in this domain aims to identify high-risk patients more accurately and to support clinical decision-making processes effectively. In Figure 7, the confusion matrix is depicted as an example, using the dependent variable \"Ex\". The confusion matrix serves as a useful tool for understanding the performance of the model and its predictions. Figure 7 illustrates the confusion matrix using the dependent variable \"Ex\" as an example. In the matrix:  
* TP represents the number of true positives, instances correctly predicted as \"Ex\" by the model. * FP represents the number of false positives, instances incorrectly predicted as \"Ex\" by the model while they are actually \"Alive\". * FN represents the number of false negatives, instances incorrectly predicted as \"Alive\" by the model while they are actually \"Ex\". Figure 7: 2 \(\times\) 2 Confusion matrix. * TN represents the number of true negatives, instances correctly predicted as \"Alive\" by the model. Accuracy is a measure of the overall success of the model, calculated by dividing the correctly classified data by the total number of data points. However, it is important to note that accuracy may not be an effective metric in datasets with imbalanced class distributions. Using a combination of these performance metrics provides a comprehensive evaluation of the models' performance and helps assess their effectiveness in the specific classification problem at hand. \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
Precision measures the probability that the samples predicted as positive by the model are actually positive. It provides an indication of the model's ability to avoid false positives. \[Precision=\frac{TP}{TP+FP} \tag{5}\]  
Recall, also known as sensitivity, represents the proportion of actual positive samples that are correctly predicted as positive by the model. It measures the model's ability to identify positive instances. \[Recall=\frac{TP}{TP+FN} \tag{6}\]  
The F1-score, as stated, is a measure of the model's robustness. It combines both precision and recall into a single metric, providing a balanced assessment of the model's performance. The F1-score is calculated as the harmonic mean of precision and recall, ranging from 0 to 1, where 1 indicates the best possible performance. \[F1-score=\frac{2*Recall*Precision}{Recall+Precision} \tag{7}\]  
The results from the ML and DL models created for the dependent variable \"Ex\" before applying any feature extraction technique are presented in Table 2. Based on these results, SVM and RF achieved 88% precision, LR, KNN, SVM, and RF achieved 83% F1-score and LSTM achieved an AUC value of %98. These models also showed higher accuracy compared to other models, although the exact values are not mentioned.