# Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections
## 4 Experiments and Results
### Analysis of the Impact of the Different Approaches on Interpretability  
To the best of our knowledge, there is no widely accepted metric for the interpretability of a model. The number of features, which has been studied in the previous sections, could be a good approximation, as it is related to the complexity of the model. However, the use of approaches that involve a combination of multiple submodels may impact interpretability as well, even when interpretable submodels are used. We provide an example using the models generated for the sinusoidal synthetic dataset. As a reminder, to generate this dataset we used a sinusoidal function to vary \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filter** & **Balancing** & **Seasonal Drift Approach** & **Mean AUC** & **Mean Features per Model** \\ \hline None & Oversample 2:1 & Monthly ensemble & 0.623 (0.612, 0.633) & 8.107 (7.833, 8.380) \\ \(p\)-Value & Oversample 2:1 & Monthly ensemble & 0.621 (0.611, 0.632) & 8.093 (7.822, 8.365) \\ None & Oversample 1:1 & Monthly ensemble & 0.619 (0.609, 0.628) & 7.576 (7.317, 7.834) \\ \(p\)-Value & Oversample 1:1 & Monthly ensemble & 0.618 (0.607, 0.628) & 7.532 (7.281, 7.782) \\ \(p\)-Value & Oversample 1:1 & 5-month window & 0.617 (0.609, 0.625) & 22.009 (21.809, 22.209) \\ None & Oversample 1:1 & 5-month window & 0.617 (0.609, 0.624) & 22.712 (22.502, 22.923) \\ \(p\)-Value & Oversample 2:1 & None & 0.615 (0.607, 0.623) & 31.960 (31.515, 32.405) \\ \(p\)-Value & Oversample 2:1 & Season as feature & 0.614 (0.606, 0.622) & 34.660 (34.210, 35.110) \\ \(p\)-Value & Oversample 2:1 & 5-month window & 0.614 (0.606, 0.622) & 22.738 (22.539, 22.936) \\ \(p\)-Value & Oversample 1:1 & None & 0.613 (0.605, 0.620) & 31.800 (31.392, 32.208) \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ None & None & None & 0.499 (0.498, 0.500) & 0.090 (\(\cdots\)0.012, 0.192) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top ten results obtained with decision tree models on the _Acinetobacter_ spp. dataset extracted from MIMIC-III. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filter** & **Balancing** & **Seasonal Drift Approach** & **Mean AUC** & **Mean Features per Model** \\ \hline None & Oversample 2:1 & Season as feature & 0.762 (0.759, 0.765) & 35.310 (34.884, 35.736) \\ \(p\)-Value & Oversample 2:1 & Season as feature & 0.762 (0.759, 0.764) & 33.790 (33.352, 34.228) \\ None & None & Season as feature & 0.760 (0.758, 0.763) & 21.310 (20.517, 22.103) \\ \(p\)-Value & Oversample 1:1 & Season as feature & 0.760 (0.757, 0.763) & 34.800 (34.335, 35.265) \\ None & Oversample 1:1 & Season as feature & 0.760 (0.757, 0.763) & 35.840 (35.442, 36.238) \\ \(p\)-Value & None & Season as feature & 0.759 (0.757, 0.762) & 18.150 (17.653, 18.647) \\ \(p\)-Value & None & Seasonal window & 0.758 (0.756, 0.761) & 14.435 (14.067, 14.803) \\ None & None & Seasonal window & 0.757 (0.754, 0.760) & 16.868 (16.323, 17.412) \\ None & None & None & 0.755 (0.753, 0.757) & 18.070 (17.311, 18.829) \\ \(p\)-Value & None & None & 0.755 (0.752, 0.757) & 16.120 (15.644, 16.596) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Top ten results obtained with logistic regression models on the _S. pneumoniae_ dataset extracted from MIMIC-III.