## 5 Conclusions  
This study deals with a quantitative analysis of changes in sustained phonation that has been acquired twice (with a two-year interval) in 51 PD patients. These changes are linked with progress of PD as assessed by three commonly used clinical scales. Finally, it explores a possibility of PD progress prediction based on a combination of acoustic analysis and machine learning modeling.  
Based on the reported results, we conclude that, for two years, patients' voices became more aperiodic with increased microperturbations of frequency and amplitude. Although we did not identify many significant correlations between baseline values of phonatory features and changes in clinical scores, the XGBoost algorithm was able to predict these changes with errors ranging from 11% (in the case of UPDRS IV) to 26% (in the case of UPDRS III). These results accent the impact of acoustic HD analysis in Health 4.0 systems. Next, we identified significant correlations between changes in phonatory features and changes in clinical scores; however, probably due to some underlying pathophysiological mechanisms and complex interdependencies, these relations are less interpretable. Finally, our results suggest that the researchers should consider acoustic analysis of corner vowel /i/ instead of the corner vowel /a/.  
Admittedly, the main limitation of this study is the small size of patient cohort. On the other hand, longitudinal studies of PD patients are very time-consuming (the patients are usually examined by several experts such as neurologists, clinical psychologists, and clinical speech therapists), physically demanding (PD is a movement disorder, therefore it requires patients to make significant effort to get into a hospital), and it is difficult to assess a large number of patients due to a low prevalence which is estimated to 1.5% for people aged over 65 years [57]. In fact, as far as we know, this is the first complex study analyzing changes in phonation and their relations with progress of PD based on such a big dataset. Moreover, it is the first study employing acoustic analysis of phonation in combination with machine learning modeling in order to predict the progress of PD. Nevertheless, our findings should be confirmed by further scientific research that will include bigger cohorts.  
Conceptualization, Z.G., J.M. and I.R.; Methodology, Z.G. and J.M.; Software, Z.G. and J.M.; Validation, Z.G.; Formal Analysis, Z.G.; Investigation, Z.G., J.M., i. e., M.M., M.K. and I.R.; Resources, [PERSON], i. e., M.M., M.K. and I.R.; Data Curation, J.M., T.K., i. e., M.M., M.K., I.R.; Writing--Original Draft Preparation, Z.G., J.M., V.Z., J.M., T.K.; Visualization, [PERSON]; Supervision, J.M., Z.S., I.R., M.F.-Z., J.B.A.-H. and P.G.-V.; Project Administration, J.M. and I.R.; Funding Acquisition, J.M., J.R., M.F.-Z., J.B.A.-H. and P.G.-V.  
This research was funded by the grant of the Czech Ministry of Health 16-30805A (Effects of non-invasive brain stimulation on hypokinetic dysarthria, micrographia, and brain plasticity in patients with [PERSON]'s disease) and the following projects: LO1401, FEDER and MEC, TEC2016-77791-C4-2-R, TEC2016-77791-C4-1-R, CENIE_TECA-PARK_55_02 INTERREG V-A Spain--Portugal (POCTEP), and TEC2016-77791-C4-4-R from the Ministry of Economic Affairs and Competitiveness of Spain. For the research, infrastructure of the SIX Center was used.  
The authors declare no conflict of interest.