## 6 Choosing the Optimal Model
### Summary of Simulation Results  
The classification metrics results (for both the training process and the simulation) are presented in Table 8. Based on these results, it can also be observed that, similar to the simulation process alone, the best results for RMA1, RMA2, and RMA3 were provided by the Net3 model, while the best results for ADL were obtained with the Net2 model. Based on the conducted analysis of the simulation results, as well as the combined results from the training process and simulation compared to actual outcomes, the optimal model was chosen as a combination of the Net2 and Net3 models. The Net2 model proved to be the best for predicting the ADL indicator (Figure 10), achieving very high accuracy and precision. Meanwhile, the Net3 model was the most effective for the RMA1, RMA2, and RMA3 indicators (Figure 11), which suggests its usefulness in assessing patients' motor progress. Based on the presented results, it can be observed that the prediction accuracy for the ADL indicator is 94.4%, which is significantly higher compared to the other indicators. The RMA1, RMA2, and RMA3 indicators achieved relatively low accuracy of 44.4%, 44.4%, and 38.9%, respectively, suggesting that the model had difficulties predicting these values. In particular, the RMA3 indicator achieved the lowest accuracy, indicating the need for further research to improve the model's prediction capabilities in this area. Overall, the model demonstrated the highest effectiveness for the ADL indicator. Figure 9: Confusion matrix for RMA1, RMA2, RMA3, and ADL (for training and simulation results)—model Net3. Figure 11: The graph of simulation accuracy for RMA and ADL results—Net3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Feature** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Cohen’s Kappa** \\ \hline N1 & RMA1 & 67.4\% & 71.1\% & 73.1\% & 64.4\% & 67.4\% \\ N1 & RMA2 & 58.7\% & 55.9\% & 62.8\% & 54.4\% & 58.7\% \\ N1 & RMA3 & 58.7\% & 57.5\% & 65.9\% & 57.3\% & 58.7\% \\ N1 & ADL & 71.7\% & 57.9\% & 45.0\% & 45.0\% & 71.7\% \\ N2 & RMA1 & 54.3\% & 33.3\% & 32.6\% & 31.7\% & 54.3\% \\ N2 & RMA2 & 50.0\% & 40.1\% & 41.0\% & 36.8\% & 50.0\% \\ N2 & RMA3 & 54.3\% & 56.3\% & 54.9\% & 54.0\% & 54.3\% \\ N2 & ADL & 93.5\% & 89.4\% & 87.1\% & 88.0\% & 93.5\% \\ N3 & RMA1 & 91.3\% & 91.7\% & 87.1\% & 88.1\% & 91.3\% \\ N3 & RMA2 & 89.1\% & 84.0\% & 80.1\% & 81.6\% & 89.1\% \\ N3 & RMA3 & 91.3\% & 92.4\% & 90.1\% & 90.0\% & 91.3\% \\ \hline N3 & ADL & 39.1\% & 20.8\% & 24.9\% & 20.6\% & 39.1\% \\ N4 & RMA1 & 0.0\% & 0.0\% & 0.0\% & 0.0\% & 0.0\% \\ N4 & RMA2 & 0.0\% & 0.0\% & 0.0\% & 0.0\% & 0.0\% \\ N4 & RMA3 & 4.3\% & 0.4\% & 9.1\% & 0.8\% & 4.3\% \\ N4 & ADL & 87.0\% & 66.2\% & 76.0\% & 69.6\% & 87.0\% \\ N5 & RMA1 & 39.1\% & 36.8\% & 31.4\% & 31.1\% & 39.1\% \\ N5 & RMA2 & 60.9\% & 57.1\% & 66.3\% & 57.9\% & 60.9\% \\ N5 & RMA3 & 39.1\% & 32.0\% & 33.9\% & 31.7\% & 39.1\% \\ N5 & ADL & 0.0\% & 0.0\% & 0.0\% & 0.0\% & 0.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The classification metric results for the training process and the simulation. The best result is marked by blue color. Figure 10: The graph of simulation accuracy for RMA and ADL results—Net2. Based on Figure 11, the following conclusions can be drawn regarding the accuracy of predicted outcomes in different categories:  
The RMA1, RMA2, and RMA3 indicators showed high accuracy, achieving 91.3%, 89.1%, and 91.3% correct results, respectively. These indicators also have a small percentage of incorrect results, ranging from 8.7% to 10.9%. For the ADL indicator, the results were less precise, with an accuracy of 26.1%, while 73.9% were incorrect predictions. This suggests difficulties in predicting the ADL indicator compared to the other variables. The combination of the Net2 and Net3 models allows for optimal results across different aspects of rehabilitation.