## 3 Results
The Matthews correlation coefficient (MCC) reaches its highest value of 83.50% with 50 features, suggesting the best overall quality of the model. A decline in MCC is observed with fewer features, reaching 73.00% with ten features. The highest F1 score (96.23%) was achieved with 50 features, indicating that the model best balances both sensitivity and specificity with this number of features. With ten features, the F1 score drops to 85.30%, representing a noticeable deterioration compared to the results obtained with a larger number of features. The highest predictive values for the positive class (PPV) and the negative class (NPV) were achieved with 50 features, being 92.00% and 91.50%, respectively. These values gradually decrease as the number of features is reduced, reaching 90.00% PPV and 91.50% NPV with 100 features. With 10 features, PPV is 85.50% and NPV is 87.50%, representing an improvement compared to 20 features, but still lower than with a larger number of features. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
\begin{tabular}{c} **Number of** \\ **Features** \\ \end{tabular} & **ACC** & **TPR** & **TNR** & **PPV** & **NPV** & **MCC** & **F1 Score** \\ \hline
267 & 90.25 & 90.00 & 95.00 & 90.00 & 90.50 & 80.50 & 93.18 \\ \hline
200 & 90.75 & 91.00 & 90.50 & 91.00 & 90.50 & 81.50 & 94.55 \\ \hline
150 & 91.25 & 91.00 & 91.50 & 91.00 & 91.50 & 82.50 & 94.59 \\ \hline
100 & 90.75 & 90.00 & 91.50 & 90.00 & 91.50 & 81.50 & 96.12 \\ \hline
50 & 91.75 & 92.00 & 91.50 & 92.00 & 91.50 & 83.50 & 96.23 \\ \hline
20 & 87.00 & 86.00 & 88.00 & 86.00 & 88.00 & 74.00 & 81.77 \\ \hline
10 & 86.50 & 85.50 & 87.50 & 85.50 & 87.50 & 73.00 & 85.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Random Forest classification results. Figure 7: Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for K_NN classification. The highest accuracy (ACC) was achieved with 50 features, 91.75%. When the number of features was reduced below 50, the accuracy slightly decreased, reaching 90.75% with 100 features, 91.25% with 150 features, and 90.75% with 200 features. With 267 features, the accuracy was 90.25%. On the other hand, with 20 features, the accuracy dropped to 87.00%, and with 10 features, it was 86.50%, indicating a significant loss of classification quality with too few features. Sensitivity (TPR) reached its highest value of 92.00% with 50 features, and gradually decreased with fewer features, reaching 90.00% with 100 features, 91.00% with 150 features, and 91.00% with 200 features. With 267 features, sensitivity was 90.00%, and with 10 features, it was 85.50%, suggesting that fewer features impact the model's ability to detect positive cases. Specificity (TNR) remained high at 50 features (91.50%) and then slightly decreased to 90.50% with 100 features, 91.50% with 150 features, and 90.50% with 200 features. The highest specificity (95.00%) was obtained with 267 features, while the lowest (87.50%) was obtained with 10 features, indicating a reduced ability of the model to classify negative cases with too few features (Figure 8). The optimal number of features for the Random Forest method that provides the best classification results is 50 features. Reducing the number of features below 50 leads to a gradual decline in model performance, particularly in terms of MCC, PPV, NPV, F1 score, and TPR. An excess of features (267 features) slightly improves some metrics, but with 10 features, the model achieves significantly worse results, indicating a substantial deterioration in classification quality. The analysis of the classification results for different classifiers, depending on the number of features, reveals significant variations in the effectiveness of the respective methods (Table 6). The highest performance was achieved by the K-Nearest Neighbors (K = 1) and Multilayer Perceptron classifiers. KNN demonstrated the best results with 50 features, attaining a highest F1 score of 96.79% and accuracy (ACC) of 96.75%.