The advantage of our study
PD
SEPARATION
First, our study separated the entire TUG test into four components and integrated them into a final weighted average ensemble classification model with different weights. The key reason for this separation was considering the different levels of sensitivity to PD/ET for the four components, which should correspond to different weights while building the ensemble classification model. Second, to evaluate our model performance, we kept 20% of our whole dataset as our independent test data. Third, unlike previous studies that focused on PD with H&Y 1–4 [