## 3 Results
\begin{table}
\begin{tabular}{c 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 & 83.00 & 76.50 & 89.50 & 76.50 & 89.50 & 66.60 & 81.80 \\ \hline
200 & 87.00 & 84.50 & 89.50 & 84.50 & 89.50 & 74.10 & 86.64 \\ \hline
150 &  
The multilayer perceptron method demonstrates a significant influence of the number of features on the performance of various classification parameters (Table 2). Both an excessive and an insufficient number of features negatively affect the model's effectiveness. The Matthews correlation coefficient (MCC), which measures the overall performance of the model, reaches its highest value (93.00%) with 100 features. With a higher number of features (200 and 267), MCC decreases slightly (90.00% and 89.00%, respectively). The lowest MCC values (75.60% and 76.10%) are observed with 20 and 10 features, indicating the negative impact of too few features on classification performance. A similar trend is observed for the positive predictive value (PPV) and negative predictive value (NPV), which reach their highest values (97.00% and 96.00%) with 100 features, confirming the optimality of this feature count for accurate classification of both classes. With fewer features, these values decrease slightly, reaching 85.50% for PPV and 90.50% for NPV with ten features. The F1 score also reaches its highest value (96.50%) with 100 features. Reducing the number of features below 50 results in a decline in the F1 score, which reaches its lowest value (87.45%) with 20 features. Although the F1 score with 267 features is 94.44%, it is lower than with 100 features. The accuracy (ACC) of the model is highest with 100 features (96.50%), suggesting that this number of features provides the best balance between correctly classifying positive and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Features** & **ACC** & **TPR** & **TNR** & **PPV** & **NPV** & **MCC** & **F1 Score** \\ \hline
267 & 94.50 & 93.50 & 95.50 & 93.50 & 95.50 & 89.00 & 94.44 \\ \hline
200 & 95.00 & 94.50 & 95.50 & 94.50 & 95.50 & 90.00 & 95.00 \\ \hline
150 & 93.50 & 93.50 & 93.50 & 93.50 & 93.50 & 87.00 & 93.50 \\ \hline
100 & 96.50 & 97.00 & 96.00 & 97.00 & 96.00 & 93.00 & 96.50 \\ \hline
50 & 94.25 & 95.50 & 93.00 & 95.50 & 93.00 & 88.50 & 94.34 \\ \hline
20 & 87.75 & 85.50 & 90.00 & 85.50 & 90.00 & 75.60 & 87.45 \\ \hline
10 & 88.00 & 85.00 & 90.50 & 85.50 & 90.50 & 76.10 & 87.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multilayer Perceptron classification results. Figure 4: Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Naive Bayes classification. negative cases. With 267 and 200 features, the accuracy decreases slightly to 94.50% and 95.00%, indicating a slight deterioration in performance due to feature redundancy. When the number of features is reduced below 50, accuracy drops significantly, reaching its lowest value (87.75%) with 20 features. A similar trend is observed for sensitivity (TPR), which reaches its highest value (97.00%) with 100 features, demonstrating the model's best ability to correctly classify positive cases. Sensitivity gradually decreases as the number of features is reduced, reaching 85.00% with ten features, showing the loss of critical information necessary for effective classification. An excessive number of features, such as 267, also causes a decrease in sensitivity to 93.50%. Specificity (TNR) remains high (95.50%) with 200 and 267 features, indicating the model's strong ability to detect negative cases with a higher number of features. However, as the number of features decreases, specificity drops to 93.00% with 50 features and 90.50% with 10 features (Figure 5). The presented results suggest that 100 features is the optimal choice, providing the highest values for all evaluated metrics.