## 7 Results of Experiments
### Raw Feature Integration  
The results of data integration were presented, first of all, in the case of applying loose feature integration after pre-selecting features as part of pre-defined separate systems for PD identification. An 11-component feature vector was obtained at the feature pre-selection stage, taking into account the features generated based on three speech signal generation aspects (phonation, articulation and prosody) obtained at the feature pre-selection stage. Classifier effectiveness is shown using the error matrix in Figure 7. A confusion matrix  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Classifier**} \\ \cline{2-7}
**Modalities** & \multicolumn{2}{c}{**1-nn**} & \multicolumn{2}{c}{**SVM**} \\ \cline{2-7}  & **ACC [\%]** & **Se [\%]** & **Sp [\%]** & **ACC [\%]** & **Se [\%]** & **Sp [\%]** \\ \hline \hline Phonation & 91.7 & 91.7 & 91.7 & 85.4 & 100 & 70.8 \\ Articulation & 89.8 & 89.4 & 88.1 & 88.9 & 88.9 & 88.9 \\ Prosody & 64.6 & 66.7 & 62.5 & 72.9 & 75.0 & 70.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effectiveness of classifiers for the individual modalities for feature pre-selection of the individual speech model.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Classifier**} \\ \cline{2-7}
**Modalities** & \multicolumn{2}{c}{**1-nn**} & \multicolumn{2}{c}{**SVM**} \\ \cline{2-7}  & **ACC [\%]** & **Se [\%]** & **Sp [\%]** & **ACC [\%]** & **Se [\%]** & **Sp [\%]** \\ \hline \hline Phonation & 87.5 & 95.8 & 79.2 & 83.1 & 100 & 66.7 \\ Articulation & 87.8 & 88.9 & 86.7 & 82.2 & 84.4 & 80.0 \\ Prosody & 60.4 & 54.2 & 66.7 & 70.8 & 75.0 & 66.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effectiveness of classifiers for the individual modalities for full dimensional vectors of the speech model.  
represents the prediction summary in matrix form. It shows how many predictions are correct and incorrect per class. It helps in understanding the classes that are being confused by the model as belonging to another class. The column on the far right represent the percentages of all patients recognized as belonging to each class that are correctly (blue) and incorrectly (orange) classified. Naturally, the values in blue are sensitivity and specificity, respectively [98].  
The obtained results enable a conclusion that a feature vector structure involving a simple combination of features generated based on different speech signal modeling systems does not cause a significant increase in system accuracy. It is most probably associated with the fact that the selection process in feature subsets is related to a specifically preset feature vector, optimizing it in relation to the applied classifier. Therefore, the structure of the feature vector at the property fusion level should be defined based on a separate process of selecting features dedicated to the new set of descriptors. The usefulness of features selected based on a separate speech signal generation aspect is high, but only in relation to this specific task. Combining them will lead to a change in usefulness within a fusion-based system. The essence of these approaches is different.