## 4 Discussion
The KNN model with 50 features achieved an accuracy of 96.75%, which is the highest result in the table. In addition, a high sensitivity of 97.50% and a specificity of 96.00% were obtained. In a very similar way, the MLP model with 100 features achieved an ACC of 96.50%, TPR of 97.00%, and TNR of 96.00%. These results indicate that these models are characterized by excellent performance in identifying both positive and negative cases, with high accuracy and balanced sensitivity and specificity. Compared to these results, the study by [31] shows that using different feature selection methods (such as ANOVA, RELIEF or RFE) in combination with SVM and NuSVM classifiers gave slightly worse results. In all these cases, the accuracy was 95%, and the sensitivity and specificity were 95%. Although these results are robust and show a balance between detecting positive and negative cases, they are still lower than those achieved by KNN and MLP. On the other hand, in the study by [32], where fewer features were used (six features), the 5-NN and QDA models achieved an accuracy of 90%, which is the lowest result in the table. The 5-NN model also achieved a relatively low sensitivity of 85.71%, despite a very high specificity of 95.36%. The QDA model had a higher sensitivity (87.50%), but a lower specificity (92.50%). Although both models achieved equal accuracy, the differences in sensitivity and specificity indicate some trade-offs that had to be accepted as a result of using fewer features. The table below (Table 8) compares the obtained research results with the results of other authors who conducted similar studies and achieved high effectiveness using their methods. The quality of the models built should be assessed as high. Considering the total classification accuracy (ACC), it can be seen that the obtained result is the highest and that most of the results presented in the table are much lower. Analyzing the data, it can be stated that the presented method stands out from similar studies and shows high diagnostic potential. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No. in Ref.** & \begin{tabular}{c} **Number of** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **Feature** \\ **Selection** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Classification** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **ACC (\%)** \\ \end{tabular} & \begin{tabular}{c} **TPR (\%)** \\ \end{tabular} & \begin{tabular}{c} **TNR (\%)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Current \\ research \\ \end{tabular} & 50 & - & KNN & 96.75 & 97.50 & 96.00 \\ \hline \begin{tabular}{c} Current \\ research \\ \end{tabular} & 100 & - & MLP & 96.50 & 97.00 & 96.00 \\ \hline \begin{tabular}{c} [31] \\ \end{tabular} & 17 & ANOVA & NuSVM & 95.00 & 95.00 & 95.00 \\ \hline \begin{tabular}{c} [31] \\ \end{tabular} & 18 & RELIEF & SVM & 95.00 & 95.00 & 95.00 \\ \hline \begin{tabular}{c} [31] \\ \end{tabular} & 18 & RELIEF & NuSVM & 95.00 & 95.00 & 95.00 \\ \hline \begin{tabular}{c} [31] \\ \end{tabular} & 18 & RFE & SVM & 95.00 & 95.00 & 95.00 \\ \hline \begin{tabular}{c} [31] \\ \end{tabular} & 18 & RFE & NuSVM & 95.00 & 95.00 & 95.00 \\ \hline \
\begin{tabular}{c} [32] \\ \end{tabular} & 6 & POE + ACC & 5-NN & 90.00 & 85.71 & 95.36 \\ \hline
\begin{tabular}{c} [32] \\ \end{tabular} & 6 & LSR & QDA & 90.00 & 87.50 & 92.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of classification results in relation to the author’s previous research. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  
The results presented in this work demonstrate the diagnostic potential of the proposed methods, which could serve as the basis for developing a screening procedure routinely performed for individuals in a specific age group at risk of bone tissue loss.