## 2 Materials and Methods
### Development and Performance Evaluation of the Prediction Model  
This study developed prediction models for distinguishing hypokinetic dysarthria from presbyphonia by using CART, GBM, XGBoost, and random forest and compared the area under the curve (AUC), accuracy, sensitivity, and specificity of the development models to identify the prediction performance of them. The collected data were randomly divided into a training dataset (70%) and a test dataset (30%). A 5-fold cross-validation (CV) was performed only on the training data, and the test dataset was used to evaluate the prediction performance. The ensemble algorithm contains randomness, and models were developed by setting the seed to 123456789. The prediction performance of models was compared using the AUC, accuracy, sensitivity, and specificity of each model. The AUC summarizes the entire location of the receiver operating characteristic (ROC) curve rather than depending on a specific operating point. The closer the AUC value is to 1, the better the predictive power of the model. Accuracy indicates the percentage (OR proportion) of the subjects successfully predicted by each model. Sensitivity is the percentage of the true positives out of all the samples, while specificity means the percentage of the true negatives. This study defined the model with the best prediction performance as a model showing the highest accuracy and AUC while sensitivity and specificity values were 0.7 or higher, and chose it as the final model to predict hypokinetic dysarthria. All analyses  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Explanation** \\ \hline CPP (dB) & Defined as the height of that peak relative to a regression line through the overall cepstrum. \\  & The better the quality of voice, the higher the score. \\  & LHR is the ratio of low (below 4000 Hz) versus high (above 4000 Hz) frequency spectral \\ L/H ratio (dB) & energy (spectral tilt). Threshold is 4000 Hz and its score is low in abnormal voice. Higher \\  & values reflect greater low frequency spectral energy, which is commonly found in normal \\  & voices. \\ L/H ratio\_SD & Standard deviation of L/H ratio \\ L/H ratio Max (dB) & Maximum value of L/H ratio \\ L/H ratio Min (dB) & Minimum value of L/H ratio \\  & Defined as average frequency of CPP which is in the range of 60–300 Hz of vocal sound \\  & among analyzed voices. \\  & Its score is high in abnormal voice. The CSID estimated dysphonia severity is theoretically a \\  & number between 0 and 100, with 100 being rated the most severe. \\ CPP Max (dB) & Maximum value of CPP \\  & Minimum value of CPP \\  & Mean CPP F0 (Hz) \\  & Mean CPP F0 SD (Hz) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The definitions of Cepstral and Spectral measures.  
were performed using the package (xgboost, randomForest, gbm, rpart, and tree) in R version 3.5.1 (Foundation for Statistical Computing, Vienna, Austria). The package can be downloaded from the web ([[https://cran.r-project.org/web/packages/](https://cran.r-project.org/web/packages/)]([https://cran.r-project.org/web/packages/](https://cran.r-project.org/web/packages/)); accessed date: 1 March 2021).