## 5 Experimental Setup and Results Evaluation
### Performance Evaluation  
Here, we evaluate the performance of the proposed ANN and encoding-based model by comparing the obtained accuracy of each one with that of various algorithms including Fine Tree, Boosted Trees, Linear Discriminant, Naive Bayes, SVM, and KNN. As listed in Table 7, the proposed encoding-based model realizes the highest level of prediction accuracy (98.8%) with a misclassification rate of 1.2%. The developed ANN model also provides a competitive accuracy; however, it incurs 37 neurons in the hidden layer to build a suitable structure. It can be seen that both the Gaussian Naive Bayes and the Quadratic SVM also achieve the third and fifth prediction accuracy (94.8% and 96%, respectively). To further evaluate the performance of the SVM algorithm, Naive Bayes classifier, proposed ANN, and encoding-based models in more detail, a confusion matrix is drawn. The confusion matrix is used to evaluate the effectiveness of classification models by categorizing data into various classes of true/false and positive and negative rates. Figure 9 depicts the confusion matrix of the presented ANN model. It can be noticed that the confusion happens between the first and second class (stay home and emergency room) and the second and third class (emergency room and ICU). This is because there are some correlated signs attributed to different classes and differentiating them requires more intensive computations by stacking  
Figure 8: The LabVIEW front panel testing window of the proposed encoding-based model. Note that the yellow indicators signify the “ON” state for the corresponding risk factor. more hidden layers. From Figure 10, it can be seen that the best validation performance (0.0263) was achieved at epoch 54, while the set goal was 0.01. This confirms that the model is capable of providing an accuracy of 97.37% for the validation subset of data, but it utilizes a considerable number of neurons in the hidden layer (37 neurons). \begin{table}
\begin{tabular}{c c c} \hline \hline
**ML-Based Algorithm** & **Accuracy (\%)** & **Failure Prediction Rate (\%)** \\ \hline Decision Tree (DT) & 92.8 & 7.2 \\ \hline Medium Tree & 93.2 & 6.8 \\ \hline XGBoost & 94.1 & 5.9 \\ \hline RUSBoosted Trees & 92 & 8 \\ \hline Linear Discriminant & 83.7 & 16.3 \\ \hline Gaussian Naive Bayes & 94.8 & 5.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy of the proposed approaches compared to that of different ML-based multi-class classification algorithms. Figure 9: Confusion matrix of the developed ANN classifier: (**a**) training results, (**b**) validation results, (**c**) test results, (**d**) model’s overall performance evaluation. On the other hand, Figure 11 illustrates the confusion matrix of the Naive Bayes, Quadratic SVM, and proposed encoding-based algorithms. The encoding-based model achieves the lowest confusion between SH and ER and ER and ICU as it realizes the highest Positive Predictive Values (PPVs) and imposes the lowest False Discovery Rates (FDRs). Furthermore, the probability of the evaluated models, SVM, and Naive Bayes classifier is used to identify the status by computing the likelihood of each risk factor and then evaluating the prediction performance using precision, sensitivity, and F1-score as evaluation metrics. As listed in Table 8, the encoding model provides the highest prediction accuracy for all evaluation metrics. This can be explained as follows: the preprocessing of the proposed model concentrates on identifying the most crucial features to calculate RS and VS, then addressing them to specific lookup tables (LUTs). Therefore, unlike the