## 3 Results
Automatic scoring, i.e., the estimation of the values of the clinical rating scales must be viewed as a tool that can provide clinicians with an additional, unbiased, and objective information that can help them with their decision-making, not as a tool that will substitute the work of clinicians. With this in mind, the predictions made by the trained XGBoost models can be considered rather reasonable as the error of 10-20% is comparable with a deviation caused by inter/intra-rater variability. Moreover, each clinical rating scale is different. On one hand, there are complex scales such as UPDRS III describing various motor aspects of PD, and, on the other hand, there are scales specifically focusing on a subset of its symptoms, e.g., FOG-Q (gait difficulties), RBDSQ (sleep disorders), etc. This information must be taken into account when evaluating the prediction errors because, the more complex the scale is, the more difficult it becomes to predict its values. This can be seen in our results as well. The most complex of the scales was predicted with the largest prediction error. Feature importances of the SGBoost models are visualized in Figure 2. The figure shows the feature importances for all of the trained models. Feature importances quantify a relative importance of the features in the ensemble of the trained XGBoost model [47]. Therefore, the higher the value of the feature importance, the more important the feature is for the prediction of the dependent variable. With this in mind, the rationale behind this visualization is to show which features are important, and how strong that importance is, for the trained models in direction of predicting the change in the particular clinical rating scales in the horizon of two years given the acoustic features at the baseline. Based on these graphs, we can conclude that the estimation of UPDRS III change requires a complex parametrization because, in all scenarios, at least 13 acoustic features were employed. In this case, especially median NNE was not frequently used. Although the models estimate the change of UPDRS IV with the lowest error, they usually use just a few phonatory parameters. In fact, in the case of vowel /o/, we observed 11.3% estimation error based on the following three phonatory features: GNE (Q2), ER (Q2), and FLUF. Generally, these features quantify quality of voicing. The best estimation of the RBDSQ change is based on eight phonatory parameters extracted from vowel /i/. The most important features quantify tremor of jaw (F1 (CV)), aperiodicity (FLUF), and microperturbations in intensity (APQ). Finally, based on the feature importances, we can observe that the most important role in FOG-Q change estimation was played by formant frequencies quantifying tremor of the articulatory organs. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**VT** & **MAE** & **EER [\%]** & **MAE** & **EER [\%]** & **MAE** & **EER [\%]** & **MAE** & **EER [\%]** \\ \hline \multicolumn{8}{c}{UPDRS III} & \multicolumn{2}{c}{UPDDRS IV} & \multicolumn{2}{c}{RBDSQ} & \multicolumn{2}{c}{FOG-Q} \\ \hline /a/ & 8.2 \(\pm\) 2.6 & 29.1 \(\pm\) 9.2 & 1.9 \(\pm\) 0.6 & 12.9 \(\pm\) 4.1 & 2.2 \(\pm\) 1.0 & 17.6 \(\pm\) 8.1 & 3.1 \(\pm\) 0.8 & 14.7 \(\pm\) 3.8 \\ /e/ & 7.3 \(\pm\) 2.0 & 25.7 \(\pm\) 7.0 & 1.8 \(\pm\) 0.7 & 12.2 \(\pm\) 4.8 & 2.0 \(\pm\) 0.8 & 16.4 \(\pm\) 6.9 & 3.4 \(\pm\) 1.0 & 16.1 \(\pm\) 5.0 \\ /i/ & 7.4 \(\pm\) 2.7 & 26.3 \(\pm\) 9.4 & 1.9 \(\pm\) 0.8 & 12.9 \(\pm\) 5.5 & 2.0 \(\pm\) 0.7 & 16.3 \(\pm\) 6.3 & 2.9 \(\pm\) 0.7 & 13.6 \(\pm\) 3.6 \\ /o/ & 7.9 \(\pm\) 2.1 & 28.2 \(\pm\) 7.7 & 1.7 \(\pm\) 0.7 & 11.3 \(\pm\) 4.8 & 2.1 \(\pm\) 0.8 & 16.8 \(\pm\) 6.3 & 3.3 \(\pm\) 0.5 & 15.4 \(\pm\) 2.6 \\ /u/ & 7.7 \(\pm\) 2.5 & 27.2 \(\pm\) 8.8 & 2.0 \(\pm\) 0.8 & 13.8 \(\pm\) 5.5 & 2.1 \(\pm\) 0.9 & 17.3 \(\pm\) 7.2 & 2.8 \(\pm\) 0.9 & 13.2 \(\pm\) 4.5 \\ \hline \multicolumn{8}{c}{VT—vocal task; MAE—mean absolute error; EER—estimation error rate.} \\ \end{tabular}
\end{table} 
Table 5: Results of the clinical scales’ estimation.