## 3 Results and Discussion
### Individual vs. Population Predictor  
For the population predictor, the number of clusters (and, therefore, the number of local models needed) for each partition predictor are reported in Table 1, where the first row displays the results using the whole 16-week dataset. The optimal number of clusters in the day predictor is independent of the volume of data used for training, always requiring eight clusters for optimum clustering performance. For the individual predictor case, a detailed list of the number of clusters of each partition and each patient is detailed in [18]. The glucose predictor ensembled for the day partition is consistent in finding eight clusters of data independently of the amount of data used to train it, suggesting a homogeneous distribution of easy to identify behaviors across the dataset. The night predictor ranges from three to five clusters as the volume of data increases. The three base clusters may represent common nighttime patterns, which appear consistently even in small datasets. The fourth and fifth clusters, however, are only identified when the data volume grows, suggesting that they are sparsely represented in the data, and several repetitions of those behaviors are required to appear in the data in order to assign them a cluster. A similar conclusion can be drawn from the number of clusters in the hypoglycemic predictor. As expected from the population approach, the number of clusters needed when clustering the whole dataset is larger than those for any single individual (see [18]), which range from five to eight for the Day Predictor, from three to five in the Night Predictor, and between one and four for the hypoglycemia predictor. The case of the hypoglycemia predictor is particularly interesting, since using data from a population yields from five to eight clusters, more than that of any individual. This leads to the conclusion that hypoglycemia predictors are more readily built with fewer sets of data than those based on data from the individual, due to hypoglycaemic patterns appearing more often among many individuals rather than repeatedly in the same individual. The accuracy performance of both predictors (trained with individual/population data) are reported in Table 2 for the initial training with 16-week data. The results are reported as mean (standard deviation), and median [25 th-75 th percentiles]. For each \(PH\), results for each partition predictor (meal, night, hypoglycemia treatment) as well as overall metrics are presented. In both cases, results show the averaged behaviour over \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Clusters**} \\ \hline
**Dataset Size** & **Day** & **Night** & **Hypoglycemic** \\
**(weeks)** & **Predictor** & **Predictor** & **Predictor** \\ \hline
16 & 8 & 5 & 8 \\
12 & 8 & 5 & 6 \\
6 & 8 & 4 & 6 \\
4 & 8 & 3 & 5 \\
2 & 8 & 3 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of clusters in each predictor in function of the training dataset size. the 10 patient cohort. The Wilcoxon rank sum test (\(p<0.001\)) was used to test statistically significance, marked with a red dot (\(\bullet\)).