## 3 Results
The classification results obtained using the K-Nearest Neighbors (K-NN) method for K = 1, as presented in the table (Table 4) based on the number of features, highlight the differences in model performance in terms of various quality metrics. The Matthews correlation coefficient (MCC) reaches its highest value of 93.50% with 50 features, indicating the model's best overall performance in this case. MCC gradually decreases as the number of features is reduced below 50, reaching 86.60% with 267 features and 63.00% with 20 features, suggesting a significant deterioration in model quality with fewer features. At 10 features, MCC increases to 70.50%, representing some improvement compared to the value observed with 20 features. Similarly, the F1 score also achieves its highest value of 96.23% with 50 features. The F1 score gradually declines as the number of features decreases, reaching 96.12% with 100 features, 94.59% with 150 features, and 94.55% with 200 features. At 20 features, the F1 score is 81.77%, and at 10 features, it increases to 85.30%, indicating a decrease in classification quality with too few features. The positive predictive value (PPV) and negative predictive value (NPV) both reach their highest values with 100 features, being 98.50% and 96.00%, respectively. These values gradually decrease as the number of features is reduced, reaching 97.50% for PPV and 96.00% for NPV with 50 features. At 20 features, PPV and NPV drop to 82.50% and 80.50%, respectively, suggesting a decline in prediction quality with too few features. At 10 features, PPV is 85.50% and NPV is 85.00%, showing a slight improvement compared to the values observed with 20 features. The highest accuracy (ACC) of 96.75% was achieved with 50 features. Accuracy slightly decreases as the number of features is reduced below 50, reaching 96.50% with 100 features and 94.75% with 150 features. However, with a further reduction to 20 features, accuracy sharply declines to 81.50%, and with 10 features, it increases to 85.25%, suggesting a significant loss in classification quality when too few features are used. The sensitivity (TPR) reaches its highest value of 98.50% with 100 features. As the number of features decreases below 100, sensitivity gradually decreases to 97.50% with 50 features, 96.50% with 150 features, and 95.00% with 267 features. Sensitivity significantly drops to 82.50% with 20 features, and then increases slightly to 85.50% with 10 features, which may indicate a better model fit to the data with this number of features. The specificity (TNR) remains high with 50 features (96.00%). With 100 features, specificity is 94.50%, and with 150 features, it is 93.00%. However, with 20 features, specificity drops to 80.50%, indicating a loss in the model's ability to correctly classify negative cases with too few features. With 10 features, specificity increases to 85.00%, representing an improvement compared to the value obtained with 20 features (Figure 7). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of** & **ACC** & **TPR** & **TNR** & **PPV** & **NPV** & **MCC** & **F1 Score** \\ \hline
267 & 93.25 & 95.00 & 91.50 & 95.00 & 91.50 & 86.60 & 93.18 \\ \hline
200 & 94.25 & 96.50 & 92.00 & 96.50 & 96.50 & 88.60 & 94.55 \\ \hline
150 & 94.75 & 96.50 & 93.00 & 96.50 & 93.00 & 89.60 & 94.59 \\ \hline
100 & 96.50 & 98.50 & 94.50 & 98.50 & 94.50 & 93.10 & 96.12 \\ \hline
50 The optimal number of features that provides the best classification results using the K-NN method is 50 features. Reducing the number of features below 50 leads to a gradual decline in performance, particularly in terms of MCC, PPV, NPV, F1, and TPR. An excess of features (267 features) results in a slight deterioration in performance, indicating that the K-NN model achieves better results with a smaller number of relevant features. The classification results using the Random Forest method (Table 5), depending on the number of features, indicate significant changes in model performance.