## 4 Discussion  
It is important to accurately distinguish presbyphonia from neurological voice disorders because presbyphonia has similar voice characteristics to hypokinetic dysarthria caused by [PERSON]'s disease. This study developed a classifier that distinguishes hypokinetic dysarthria from presbyphonia using ensemble machine learning algorithms. This study compared the prediction performance of GBM, XGBoost, random forest, and CART to find that the accuracy of random forests was the highest. It is believed that random forest showed higher accuracy than CART because the former was based on a bagging algorithm that generated various decision trees from 500 bootstrap samples [37, 38]. CART has the limitation that the variance of the model is large because, although it has a small bias, the structure of the tree model changes greatly depending on the first split variable. In other words, if n data are split n\(-\)1 times, only one value is included in each area. The large tree model created in this way is generally more likely to cause an overfitting problem [39, 40]. On the other hand, bagging tree is a tree model made by bootstrap samples, and it has the advantage of minimizing bias and effectively reducing the variance of the model. Since random forest effectively reduces the correlation of each tree model through an algorithm that gives randomness in the growing process of the bagging tree, it is believed to have higher prediction power than CART [39]. However, further studies are needed to explain why the accuracy of random forest is higher than that of GBM and XGBoost, other ensemble algorithms. The results of this study revealed that the sensitivity of XGBoost was higher than that of random forest while sensitivity indicated the percentage of true positives. Therefore, future studies are necessary to compare prediction performance by estimating diverse evaluation methods, such as sensitivity, specificity, and weight harmonic average, suitable for the analysis objective, rather than using one performance index such as accuracy. Another key finding of this study was that CPP, one of the Cepstral indices, was the most important predictor for identifying hypokinetic dysarthria in the developed prediction model including sociodemographic factors, health behavior, and acoustic-phonetic indices. Although acoustic-phonetic characteristics, such as loudness, were generally reported as important characteristics for distinguishing hypokinetic dysarthria from other disorders [41, 42], studies on pitch and quality showed inconsistent results [43, 44]. Previous  
Figure 6: Error graphs of random forest. studies [41; 42] that analyzed the voice of patients with Parkinson's disease using Spectrum showed that the increase of shimmer and jitter, decreased maximum phonation time (MPT) and harmonic-to-noise ratio (HNR), and a decrease of F0 variability were important acoustic diagnostic indices of [PERSON]'s disease. However, some studies reported results contradictory to [PERSON] et al. [41] and [PERSON] et al. [42]. [PERSON] et al. [43] reported that acoustic measurements (e.g., F0, shimmer, jitter, and HNR) were significantly different between the control group and the Parkinson's patient group. [PERSON] et al. [44] compared [PERSON]'s disease patients with healthy people and reported that MPT and HNR were significantly different between them, but jitter and shimmer were not significantly different between them. However, these previous studies used only Spectrum indices as an analysis method to determine sound quality, which was a limitation. This study analyzed major predictors while including both Spectrum and Cepstral indices and confirmed that CPP was the most important predictor. It is believed that it was because CCP could reflect the periodicity of the speech signal well regardless of the type or length of the speech.