## 5 Evaluation and Findings
### Performance Metrics  
In this paper, each dataset was split into 80% and 20% to train and test the datasets. The train dataset (using K-fold cross-validation with K = 10) was applied to optimize the model, while the test dataset was used to assess the generalizability of the tuned model on new unseen data samples. Considering the true positive (TP), true negative (TN), false positive (FP), and false negative (FN), we utilized different performance measures to evaluate the performance of the different techniques:  
* True positive (TP): the number of correctly identified positive instances inside the desired class;
* True negative (TN): the number of correctly identified negative instances outside the desired class;
* False positive (FP): the number of incorrectly predicted positive samples when the actual target was negative;
* False negative (FN): the number of incorrectly predicted negative samples when the actual target was positive.  
Accuracy: Occurs when the proportion of occurrences correctly classified by the classification learner equals the proportion of correctly predicted samples to the total number of examples, which can be calculated as follow:  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN}. \tag{17}\]  
Precision: It is one of the performance indicators that will be used to determine how many correct positive forecasts were made. So, precision measures the minority class's accuracy; then, the ratio of correctly predicted positive instances divided by the total number of positive cases predicted is utilized to compute it, using:  
\[\text{Precision}=\frac{TP}{TP+FP}. \tag{18}\]  
Recall: It is a measurement that quantifies the proportion of actual positive predictions correctly identified out of all potential positive predictions. Unlike precision, which considers the correctly predicted positives relative to all positive predictions, recall focuses on the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of grey wolves (PopSize) & 30 \\ Number of iterations (MaxIter) & 100 \\ Search domain & [0, 1] \\ Solution dimension & No. Features \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for the GWO.  
positives that were overlooked. Essentially, it signifies the extent to which the positive class is comprehensively captured, which is calculated as follows:  
\[\text{Recall}=\frac{TP}{TP+FN}. \tag{19}\]  
F1 score: In an ideal classifier, we aim for both accuracy and recall to be maximized, equating to values of one. This optimal scenario indicates that both the FP and FN are reduced to zero, highlighting the classifier's ability to make accurate and comprehensive predictions; ultimately, minimizing errors in both positive and negative classifications. As a result, we need a statistic that takes precision and recall into account. The F1 score is a precision and recall-based measure that is defined as follows:  
\[\text{F1}-\text{score}\ =\frac{2\times\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}}. \tag{20}\]  
Specificity: It is the proportion of true negative samples to all actual negative samples, which indicates the ratio of the projected presence to the total samples with heart disease presence. The specificity is expressed as follows:  
\[\text{Specificity}\ =\frac{TN}{TN+FP}. \tag{21}\]