## 8 Discussion  
In this work, different vocal feature subsets were merged to select an optimal subset of features, with attributes from three different subsets. The most reported performance metric is accuracy, followed by ROC and AUC. Unfortunately, sensitivity and specificity have been mentioned only in a few studies. Most of the phonatory approaches provide accuracy ranging between 75 and 90% [39]. In the case of articulatory approaches, accuracy ranges between 80 and 95% [59; 100; 101]. Furthermore, some articles that include the results of phonatory and articulatory approaches applied to the same corpus [64; 102] suggest that the articulatory aspects have better discriminative properties than the phonatory aspects. The results obtained within this experiment show that the features generated based separately on the phonation and articulation process are characterized by the highest diagnostic accuracy, reaching 88-90%. With speech modeling using only phonation information, a system for PD recognition was achieved with the best sensitivity rate--100%. The results obtained are comparable to the results of others in studies with similar methodology, but the authors used a much smaller number of features. What is more, this is very important since the authors have demonstrated value in the use of k-NN and SVM at a time when many researchers seek more complicated machine learning methods, often unnecessarily [103; 104]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Classifier**} \\ \hline  & **ACC [\%]** & **Se [\%]** & **Sp [\%]** \\ \hline \multicolumn{2}{c}{Fusion model based on final selection (5 features)} \\ \hline SVM (Linear kernel) & 70.8 & 50.0 & 91.7 \\ SVM (Quadratic kernel) & 77.1 & 66.7 & 87.5 \\ SVM (Cubic kernel) & 79.2 & 70.8 & 87.5 \\ SVM (Gaussian kernel) & 89.6 & 95.8 & 83.3 \\
1-nn (Euclidean Distance) & 83.3 & 79.2 & 87.5 \\
1-nn (Chebyshev Distance) & 92.2 & 91.1 & 93.3 \\
1-nn (Minkowski Distance) & 68.8 & 41.7 & 95.8 \\
1-nn (Spearman Distance) & 85.4 & 83.3 & 87.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 9: Confusion matrices accessing integrated data fusion with selection; **left:** 1-NN (Chebyshev Distance); **right:** SVM (Gaussian kernel). The prosody aspect is still limited in performing objective analysis based on the Polish corpora. It is very important to compare within the same language in this kind of research. Our PD recognition rate using a prosody system is medium (65-70% is achieved). The best result is 75% sensitivity using SVM methods in our research. In [105], the accuracy is 62.86%, the sensitivity is 63.00, and the specificity is 62.71. Another work reported 58% accuracy, sensitivity of 70.5, and 28% specificity [106]. These subsets of features were used to create a new system based on descriptor loose integration, which is a classic combination of descriptors generated at the phonation, articulation, and prosody analysis stage. It involved obtaining an 11-component feature vector. The initial results of PD recognition lead us to make a selection at the feature fusion stage. Collective feature fusion results including the AUC factor are shown in Table 7. Implementing a different feature selection process resulted in increasing the effectiveness of system performance. The additional feature selection process allowed for the evaluation of features operating in an assembly. This is because there may be such a data distribution for which each of the two features separately may be deemed noisy, but taking into account their interaction provides a chance for a correct classification. Therefore, it is evident that not all good features of a certain subset must be useful in isolation from the remaining ones. And conversely, the best variables, if considered individually, do not necessarily form an optimal subset.