Table 5.
Relative ranks of MLa models for predicting BGb levels in PHc=60 minutes.
| ML model | SUCRAd | Relative rank |
| ARMe | 41.0 | 10.4 |
| Gradually connected neural network (GCN) | 14.2 | 14.7 |
| Fully connected (FC [neural network]) | 55.7 | 8.1 |
| Light gradient boosting machine (LGBM) | 56.0 | 8.0 |
| RFf | 59.7 | 7.5 |
| GluNet | 97.8 | 1.4 |
| NNMg | 59.9 | 7.4 |
| SVMh | 49.5 | 9.1 |
| Latent variable with exogenous input (LVX) | 85.9 | 3.3 |
| Convolutional recurrent neural network multitask learning (CRNN-MTL) | 61.4 | 7.2 |
| Convolutional recurrent neural network multitask learning glycemic variability (CRNN-MTL-GV) | 54.2 | 8.3 |
| Convolutional recurrent neural network transfer learning (CRNN-TL) | 44.5 | 9.9 |
| Convolutional recurrent neural network single-task learning (CRNN-STL) | 32.5 | 11.8 |
| k-Nearest neighbor (kNN) | 42.5 | 10.2 |
| DTi | 4.5 | 16.3 |
| AdaBoost | 24.1 | 13.1 |
| XGBoostj | 66.5 | 6.4 |
aML: machine learning.
bBG: blood glucose.
cPH: prediction horizon.
dSUCRA: surface under the cumulative ranking.
eARM: autoregression model.
fRF: random forest.
gNNM: neural network model.
hSVM: support vector machine.
iDT: decision tree.
jXGBoost: Extreme Gradient Boosting.