## 3 Results  
The classification performance was evaluated using different numbers of features. A total of 267, 200, 150, 100, 50, 20, and 10 features were used to build the classifiers, respectively. Five learning methods were used: Naive Bayes (NBC), Multilayer Perceptron (MP), Hoeffding Tree (HT), K-Nearest Neighbors (1-NN), and Random Forest (RF). A ten-fold cross-validation was used for the classification process of each method. To evaluate the accuracy of the classifiers, the following metrics were used:  
* Overall classification accuracy (ACC) measures the percentage of all cases that were correctly classified;
* True positive rate (TPR) measures the percentage of true positive cases that were correctly identified by the model;
* True negative rate (TNR) measures the percentage of true negative cases that were correctly identified by the model;
* Positive predictive value (PPV) measures the percentage of positive predicted examples that were actually positive;
* Negative predictive value (NPV) measures the percentage of negative predicted examples that were actually negative;
* A generalized version of the Pearson correlation coefficient evaluates the agreement between the model predictions and the actual classes (MCC);  
Figure 3: (**a**) Visualization of the distribution of feature values: (**a**) the first in the ranking, (**b**) the last in the ranking. red—values for osteoporosis tissue, blue—values for healthy tissue. F1-score combines precision and sensitivity into one metric, taking into account the balance between these two parameters. The classification results allow us to assess the effectiveness of each classifier depending on the number of features used. The obtained classification results are presented below. The best results for each method are marked in gray in the table. Analyzing the data presented in Table 1, a clear influence from the number of features on the performance of various metrics is observed. A larger number of features, such as 267, leads to lower values for most metrics compared to scenarios where the number of features is reduced. Reducing the number of features to 50, and even to 20 or 10, results in a significant improvement across most metrics, although some indicators exhibit slight declines with a very small number of features. Positive predictive values (PPVs) and negative predictive values (NPVs) also show higher results with fewer features. The maximum values of 87.00% for PPVs and 90.00% for NPVs are achieved with 50 features. Further reductions to 20 or 10 features result in slight declines, yet these values remain high. Notably, the Matthews correlation coefficient (MCC) is highly sensitive to the number of features, with the lowest value (66.60%) observed at 267 features and the highest (77.00%) at 50 features, followed by a minor decrease to 76.50% for 20 and 10 features. The F1 score is the lowest at 267 features (81.80%), peaks at 88.33% with 50 features, and decreases marginally to 88.02% for 20 and 10 features. This indicates that 50 features represent an optimal balance between model accuracy and complexity. The accuracy (ACC) reaches its lowest value at 267 features (83.00%), while the highest result (88.50%) is observed with 50 features, followed by a minimal decrease to 88.25% for 20 and 10 features. A similar trend is evident for sensitivity (TPR), which increases as the number of features decreases, peaking at 87.00% with 50 features and then slightly dropping to 86.50% for 20 and 10 features. Specificity (TNR), on the other hand, remains constant at 90.00% for 50, 20, and 10 features, which represents the highest value, compared to the lowest value (89.50%) at 267 features (Figure 4). In conclusion, reducing the number of features to 50 emerges as the most optimal choice. Further reduction to 20 or 10 features can be advantageous in terms of computational efficiency, as it results in only a slight degradation of performance. Conversely, a very high number of features (267) significantly lowers model quality, particularly in terms of MCC and F1 score.