## 4 Experimental Results
### Performance Metrics  
Accuracy measurements from model evaluation metrics were used to compare the results and make better decisions. Accuracy is a measure that indicates the proportion of correctly predicted classes compared to the true classes. For example, accuracy is presented as the ratio of the number of cases predicted as benign for PCa by the model to all truly benign cases, and the number of cases predicted as malignant by the model to all truly malignant cases. The classification accuracy value is obtained from the metrics specified in the confusion matrix shown in Table 1.  
True Positive (_TP_): Represents the quantity of data belonging to the positive class that is correctly classified by the classifier.  
True Negative (_TN_): Represents the quantity of data belonging to the negative class that is correctly classified by the classifier.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Predicted** & \\ \hline \multirow{2}{*}{**Actual**} & \multicolumn{2}{c}{**Positive**} & **Negative** \\ \cline{2-4}  & Positive & _TP_ & _FP_ \\ \multicolumn{1}{c}{Negative} & _FN_ & _TN_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Two-label confusion matrix.  
Figure 7: Graph of Tanh and RELU activation functions.  
False Positive (_FP_): Refers to data that actually belong to the negative class but are incorrectly classified as the positive class.  
False Negative (_FN_): Refers to data that actually belong to the positive class but are incorrectly classified as the negative class.  
Accuracy: To evaluate the success of the proposed model, the accuracy value is calculated by dividing the number of correct predictions by the total number of data points, as shown in Equation (2).  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{2}\]  
Precision: The cost of false positive predictions is especially important. Precision is an important criterion for determining the effectiveness of a model and is calculated as shown in Equation (3).  
\[Precision=\frac{TP}{(TP+FP)} \tag{3}\]  
Recall: Recall is a useful measure when the cost of predicting false negatives is high. It is expected to be as high as possible for efficient performance and is calculated as shown in Equation (4).  
\[Recall=\frac{TP}{(TP+FN)} \tag{4}\]  
F1 score: This value provides the harmonic mean of precision and recall and is calculated as shown in Equation (5).  
\[F1\;score=2*\frac{(precision\;*\;recall)}{(precision+recall)} \tag{5}\]  
Receiver Operating Characteristic (ROC) Curve: A graphical tool used to evaluate the performance of a model in classification problems. Specifically, it is used to visualize the sensitivity and specificity of the model. The ROC curve shows the relationship between sensitivity and specificity at different thresholds. The X-axis shows sensitivity, defined as the false positive rate (FPR) ratio, and the \(Y\)-axis shows specificity, defined as the true positive rate (TPR) ratio. Area under the ROC curve (AUC): Refers to the area under the ROC curve. The AUC value can range from 0 to 1. If a model's AUC value is close to 0.5, it indicates that the model is performing random classification. However, as the AUC value approaches 1, the model's performance improves.