CNN
Model description
Convolutional Neural Networks (CNNs) have been a cornerstone in image recognition tasks but have also found utility in time-series analysis. The main idea behind a CNN is the use of convolutional layers that automatically and adaptively learn spatial hierarchies of features. In the context of CGM data, CNNs can capture spatial (time-adjacent) dependencies in the glucose measurements, making them well-suited for forecasting tasks.
The best hyperparameters after optimization are the following:
| Hyperparameter | Value |
|---|---|
| n_conv_layers | 3 |
| filters | 160 |
| kernel_size | 3 |
| activation | relu |
| n_dense_layers | 2 |
| dense_size | 352 |
More results and loss curves
Aside from the scores reported in the overview, we show here the Confusion Matrix and a regression example for our network
