## 3 Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature Set** & **MCC** & **ACC [\%]** & **SEN [\%]** & **SPE [\%]** & **Feat** \\ \hline conventional on-surface & 0.83 \(\pm\) 0.18 & 91.19 \(\pm\) 9.65 & 93.00 \(\pm\) 15.52 & 70.00 \(\pm\) 0.46 & 1 \\ conventional in-air & 0.95 \(\pm\) 0.10 & **97.14 \(\pm\) 5.71** & 95.50 \(\pm\) 9.07 & 100.00 \(\pm\) 0.00 & 1 \\ conventional together & 0.95 \(\pm\) 0.11 & **97.14 \(\pm\) 5.71** & 95.50 \(\pm\) 9.07 & 100.00 \(\pm\) 0.00 & 1 \\ FD on-surface & 0.95 \(\pm\) 0.12 & 87.14 \(\pm\) 13.48 & 82.00 \(\pm\) 21.24 & 90.00 \(\pm\) 30.00 & 1 \\ FD in-air & 0.95 \(\pm\) 0.13 & 81.43 \(\pm\) 12.86 & 71.50 \(\pm\) 30.83 & 60.00 \(\pm\) 48.99 & 3 \\ FD together & 0.95 \(\pm\) 0.14 & 81.43 \(\pm\) 15.71 & 69.50 \(\pm\) 32.13 & 70.00 \(\pm\) 45.83 & 2 \\ all on-surface & 0.95 \(\pm\) 0.15 & 88.33 \(\pm\) 14.06 & 89.00 \(\pm\) 22.11 & 70.00 \(\pm\) 45.83 & 2 \\ all in-air & 0.95 \(\pm\) 0.16 & **97.14 \(\pm\) 5.71** & 95.50 \(\pm\) 9.07 & 100.00 \(\pm\) 0.00 & 1 \\ all together & 0.95 \(\pm\) 0.17 & **97.14 \(\pm\) 5.71** & 95.50 \(\pm\) 9.07 & 100.00 \(\pm\) 0.00 & 1 \\ \hline \hline MCC—[PERSON]’s correlation coefficient; ACC—accuracy; SEN—sensitivity; SPE—specificity; feat—number of features important for the trained model (i.e., feature importance of the feature > 0.0); The feature importances, as well as the exact names of these features, are summarized in the text. \\ \end{tabular}
\end{table} 
Table 6: Results of multivariate binary classification analysis (PD/HC). rating scales, the table shows the results achieved using the trained models and the associated feature importance values. All obtained results are discussed in the following section. Considering EER as our performance evaluation metric, the following results are worth pointing out. In the case of UPDRS V, the lowest EER was achieved using a single FD-based feature--specifically, the standard deviation of vertical velocity (\(\alpha=0.1\)) computed for the on-surface movements (\(12.51\pm 7.55\%\)). The same feature was selected when both FD and conventional features were considered while building the model. In general, all models achieved an EER of around 12-13%. In comparison with the conventional features, the FD-based features performed better, with a difference of about 1%. In terms of the specific features important for the trained models, the following feature importances were returned by the models: conventional on-surface (vertical normalized jerk (mean) of the repetitive word _lektorka_), conventional in-air (vertical velocity (mean) of the sentence), conventional together (vertical velocity (mean) of the sentence), FD on-surface (vertical velocity (std) \(\alpha=0.1\) of the sentence), FD in-air (vertical velocity (median) \(\alpha=0.3\) of the sentence), FD together (vertical velocity (std) \(\alpha=0.1\) of the sentence (on-surface; \(\text{FI}=0.50\)), vertical velocity (median) \(\alpha=0.3\) of the sentence (in-air; \(\text{FI}=0.50\))), all on-surface (vertical velocity (std) \(\alpha=0.1\) of the sentence), all in-air (vertical velocity (median) \(\alpha=0.3\) of the sentence), and all together (vertical velocity (std) \(\alpha=0.1\) of the sentence (on-surface; \(\text{FI}=0.50\)), vertical velocity (median) \(\alpha=0.3\) of the sentence (in-air; \(\text{FI}=0.50\))). With respect to PD duration, the lowest EER was achieved using conventional handwriting features computed for both on-surface as well as in-air movements (\(23.64\pm 7.55\%\)).