## 3 Results
Increasing the number of features to 200 or 267 still yields satisfactory results, but leads to a slight decrease in model efficiency. On the other hand, reducing the number of features too much (to 20 or 10) causes a clear deterioration in performance, indicating the loss of important information. Therefore, selecting the appropriate number of features, such as 100, appears to be the best compromise between model effectiveness and complexity. For Hoeffding Tree classification, the Matthews correlation coefficient (MCC) achieves its highest value of 77.50% with 50 features. Reducing the number of features below 50 results in a decrease in MCC to 76.50% with 20 and 10 features, and with 267 features, the MCC drops to only 66.00%, indicating that an excess of features can lead to a degradation of the overall model quality. A similar trend is observed in the F1 score, which reaches its highest value of 88.57% with 50 features. With 20 and 10 features, the F1 score is 88.02%, reflecting only a minimal decrease in performance with fewer features. However, with 267 features, the F1 score is reduced to 81.59%. Similar relationships are evident in the predictive values for the positive class (PPV) and the negative class (NPV). The highest values of PPV (87.00%) and NPV (90.50%) are achieved with 50 features. With fewer features, these values slightly decrease, reaching 86.50% for PPV and 90.00% for NPV with 20 and 10 features. However, with 267 features, PPV and NPV are reduced to 76.50% and 89.00%, respectively, indicating a decline in prediction quality (Table 3). Figure 5: Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Multilayer Perceptron classification. The highest accuracy (ACC) is achieved with 50 features (88.75%), suggesting that this number of features provides the best balance between correctly classifying both positive and negative cases. Reducing the number of features below 50 results in only a minimal decrease in accuracy (88.25% with 20 and 10 features); however, with 267 features, the accuracy drops to 82.75%, indicating a negative impact of excessive features on the model's performance. Sensitivity (TPR) increases as the number of features is reduced, reaching its highest value of 87.00% with 50 features. With 267 features, sensitivity drops to only 76.50%, showing that an excess of features reduces the model's ability to correctly classify positive cases. Reducing the number of features below 50 results in a gradual decline in sensitivity, reaching 86.50% with 20 and 10 features. Specificity (TNR) remains high with 50 features (90.50%) and does not change significantly with further reduction in the number of features, reaching 90.00% with 20 and 10 features. In contrast, with 267 features, specificity is 89.00%, representing a slight decrease (Figure 6). The optimal number of features, which provides the best results across most metrics, is 50 features. Reducing the number of features below 50 causes only a slight deterioration in performance, while an excess of features (267) leads to a decline in model effectiveness, particularly in terms of MCC, PPV, NPV, and F1 score. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Number of** & **ACC** & **TPR** & **TNR** & **PPV** & **NPV** & **MCC** & **F1 Score** \\ \hline
267 & 82.75 & 76.50 & 89.00 & 76.50 & 89.00 & 66.00 & 81.59 \\ \hline
200 & 86.50 & 84.00 & 89.00 & 84.00 & 89.00 & 73.10 & 86.14 \\ \hline
150 & 87.00 & 85.50 & 88.50 & 85.50 & 88.50 & 74.00 & 86.78 \\ \hline
100 & 87.25 & 86.00 & 88.50 & 86.00 & 88.50 & 74.50 & 87.09 \\ \hline
50 & 88.75 & 87.00 & 90.50 & 87.00 & 90.50 & 77.50 & 88.57 \\ \hline
20 & 88.25 & 86.50 & 90.00 & 86.50 & 90.00 & 76.50 & 88.02 \\ \hline
10 & 88.25 & 86.50 & 90.00 & 86.50 & 90.00 & 76.50 & 88.02 \\ \hline \end{tabular}
\end{table} 
Table 3: Hoeffding Tree classification results. Figure 6: Changes in the values of ACC, TPR, and TNR parameters depending on the number of features for Hoeffding Tree classification.