## 6 Automatic Learning Approach
The Receiver Operating Characteristic (ROC) curve was employed [59] to assess the effectiveness of various hyperparameter configurations, specifically, the number of neighbors used. The ROC curve is a visual depiction that demonstrates the diagnostic capability of a binary classifier system as the discrimination threshold changes [60]. By graphing the sensitivity (true positive rate) against the specificity (false positive rate), it was able to determine the ideal number of neighbors that maximized the area under the curve (AUC), achieving a balance between sensitivity and specificity. Figure 10 displays the ROC curve with repeated cross-validation, indicating the optimal number of neighbors as seven. The AUC value for this configuration is 0.993. Figure 8: Classification model approach. Figure 9: Implemented cross-validation approach [57]. Therefore, to determine the best-performing model on the train set, the optimized model was retrained using the train-validation set, which combines the train and validation set. Ultimately, the model was applied to the unseen test set to evaluate its performance. The model's performance was evaluated using the confusion matrix and statistical features. Table 7 presents the statistical metrics for assessing the model's performance. The confusion matrix is a tool utilized to assess the effectiveness of a classification model by comparing the predicted labels with the true labels [61]. The decomposition of the confusion matrix is as follows:  
* **True Negatives (TN)**: There were 21 instances in which the model accurately predicted \"No\". * **False Positives (FP)**: There were 4 occurrences where the model made an incorrect prediction of \"Yes\" when the true label was \"No\". * **False Negatives (FN)**: These refer to the instances where the model incorrectly predicted a negative outcome (\"No\") when the actual label was a positive outcome (\"Yes\"). In this case, there were no instances of false negatives. * **True Positives (TP)**: There were 27 instances where the model accurately predicted \"Yes\". The confusion matrix assesses the classification model's performance by comparing predicted and actual labels. The model accurately classified 21 instances as incompatible (No) and 27 instances as compatible (Yes), leading to a high overall accuracy. The model exhibited four instances of false positives, where it erroneously classified instances as \"Yes\" instead of \"No\", and zero instances of false negatives, indicating flawless recall with \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Information** \\ \hline Number of K-neighbors & 7 \\ Accuracy & 0.923 \\
95\% CI & (0.815, 0.979) \\ Kappa & 0.845 \\ McNern’s Test \(p\)-Value & 0.134 \\ Sensitivity & 0.84 \\ Specificity & 1 \\ Positive Predictive Value & 1 \\ Negative Predictive Value & 0.87 \\ Prevalence & 0.481 \\ Detection Rate & 0.403 \\ Detection Prevalence & 0.404 \\ Balanced Accuracy & 0.92 \\ Positive Class & No \\ \hline \hline \end{tabular}
\end{table} 
Table 7: KNN model results. Figure 10: ROC curve. Hyperparameter optimization. a sensitivity of 100%. The model has a specificity of 84%, accurately identifying most negative cases. Additionally, it has a precision of 87% for compatible (Yes) predictions, indicating that 27 out of 31 \"Yes\" predictions were correct. Overall, the model demonstrates robust performance, particularly in accurately identifying positive cases, albeit with a slight inclination to incorrectly predict compatible (Yes) cases. The model's accuracy, the proportion of accurate predictions relative to the total number of samples, was 0.923. The confidence interval (CI) of the accuracy shows the predicted range of values within which the accuracy will fall, in this case (0.815, 0.979), indicating a high confidence level in the estimated accuracy. Kappa denotes the Kappa statistic, which quantifies the agreement between the expected and actual class labels, considering the probability of chance agreement. A Kappa score 0.845 indicates significant agreement outside chance [62].