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Table 1 The LOOCV testing correlation (\(\rho\)) and MAE of the proposed deep models and Gradient Tree Boosting are reported for single models and the ensemble of two or three models of the deep models

From: Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III

Method \(\rho\) MAE
Single Gradient Tree Boosting 0.61 7.85
Dual-channel LSTM, hand-crafted features 0.62 7.50
Dual-channel LSTM, hand-crafted features, with transfer learning 0.67 6.85
1D CNN-LSTM for raw signals 0.70 6.93
2D CNN-LSTM for time–frequency data 0.67 7.11
Ensemble Dual-channel LSTM, hand-crafted features, with transfer learning
1D CNN-LSTM for raw signals
0.77 6.04
Dual-channel LSTM, hand-crafted features, with transfer learning
2D CNN-LSTM for time–frequency data
0.76 5.99
1D CNN-LSTM for raw signals
2D CNN-LSTM for time–frequency data
0.74 6.54
Dual-channel LSTM, hand-crafted features, with transfer learning
1D CNN-LSTM for raw signals
2D CNN-LSTM for time–frequency data
0.79 5.95
  1. The correlation was significant for all models (i.e., \(\textit{p}< 0.001\))