## 4 Results & Discussion
### Feature Analysis  
In order to better understand the differences in the results between corpora, we analyzed the feature importance per vowel, corpora, and coefficient type using the hyperparameters of the best-performing classifiers from the baseline experiments. The feature importance values are calculated as the mean of accumulation of the impurity decrease at each decision tree trained in the random forest of the employed classification library [57]. The higher the average decrease in impurity caused by a feature during the training process, the higher the importance. The rankings in Table 17 depict the order of coefficients from most to least important in classification considering only the models trained in the baseline trials corresponding to the highest accuracy for each corpus. These were calculated using a system of votes: for each repeated experiment in each corpus, the coefficient importances were averaged between each fold. The top 12 most-voted features of all 3 corpora and both coefficient calculation toolboxes appear in Table 17. Although this analysis does not point out a specific confounding factor, it provides support for some other conclusions obtained in this study.  
For DARTH-VAT-based trials, the most significant features in the Neurovoz and mPower are the means and variances of MFCC coefficients and their derivatives. On the other hand, the most important features when using ItalianPVS are related to complexity, noise, and shimmer. In most of our experiments, ItalianPVS classifiers perform with the highest accuracy. An explanation of this may be that, as mentioned in Section 2.4, the dysphonia of PwPD in ItalianPVS is perceptually more evident than those in mPower and Neurovoz (hence the importance of features which characterize voice quality).  
For AVCA-based trials, nearly all of the top-ranked coefficients of Neurovoz are also spectral-cepstral, with some MS features. As such, AVCA-based classification accuracy improved more from speaker-wise to record-wise fold separation experiments compared to their DARTH-based counterparts. Again, ItalianPVS has different top features than the other two corpora. Nearly all of its top 12 coefficients are related to the complexity of the signal. This finding also explains why the accuracy of ItalianPVS classifiers only minimally changed between the baseline and record-wise trials. The mPower feature ranking is interesting because the top seven features are all related to the MS. In the mPower classifiers, AVCA coefficients performed slightly better than DARTH-VAT coefficients, which implies that in larger corpora, modulation spectrum characteristics may contain important information as to whether or not the speaker has PD.  
Figure 13 includes the boxplots of the most important features for each data set in the vowel /a/. It can be observed that the distributions of the most important features tend to overlap less between classes for ItalianPVS, in comparison to the other two corpora.  
Figure 12: Classification accuracy (%) of cross-corpora trials. In each experiment, training with Neurovoz (yellow) led to higher accuracy than training with ItalianPVS (gray).  
Figure 13: Boxplots of the phonatory features included in Table 17 in speakers with PD and HC. Only features for vowel \(/\mathrm{a}\mathrm{/}\mathrm{a}\mathrm{/}\mathrm{/}\mathrm{}\) are represented. To obtain this representation, the coefficients from all the participants were normalized by subtracting the mean and dividing by the standard deviation. The names of the features indicated in this figure are the ones employed in the AVCA and DARTH-VAT libraries with the suffix HC or PD indicating the class represented in the correspondent boxplot.