## 4 Results
### Classification Result  
Firstly, we built a decision tree with an overall goal to extract general information from a dataset and transform that information into a structure that can be understood by an ordinary user. A decision tree was built from physical activity parameters, i.e., the total number of steps and the groups of sedentary, light, moderate and vigorous activities, using the implementation of the c4.5 algorithm, called J48, from the Weka software package. The algorithm was started with default values, such as the confidence threshold for pruning the set to 0.25 and a minimum number of instances per leaf equal to two.  
At each node of the tree, the algorithm chose the attribute of the data that most effectively split the set of samples into subsets enriched in one class or the other. The splitting criterion was the normalized information gain. The information gain feature ranking results described in Section 4.1 had the vigorous activity minutes parameter in the first place. Hence, it could be concluded that the root of the decision tree would be the same parameter. In Figure 2, as can be observed, the results of the decision tree classification are not completely consistent with logical thinking, and in some cases, they seem contradictory.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Feature Name** & **Score** \\ \hline
1 & step count & 0.2362 \\ \hline
2 & vigorous activity minutes & 0.0505 \\ \hline
3 & moderate activity minutes & 0.0469 \\ \hline
4 & sedentary activity minutes & 0.0408 \\ \hline
5 & light activity minutes & 0.0127 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficient feature ranking.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Feature Name** & **Score** \\ \hline
1 & vigorous activity minutes & 0.1435 \\ \hline
2 & moderate activity minutes & 0.1375 \\ \hline
3 & step count & 0.084 \\ \hline... &... & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information gain feature ranking.  
Accurate and reliable information is vital for effective decision making. Thus, we employed an undersampling technique to obtain reliable estimates. It is a technique used to adjust the class distribution of a dataset. For this purpose, 115 of the 215 sick children were chosen by the random selection process to obtain two equivalent ratios of sick and healthy patient classes. After undersampling, the remaining 230 patients were considered eligible and were enrolled in the study.  
The best results in the prediction of type 1 diabetes presence among children and adolescents were obtained with decision tree forests. This model enabled the prediction with the highest accuracy (86.09%), specificity (84.35%), and precision (84.87%). The PNN also showed high accuracy (84.35%) and the highest sensitivity (89.57%), but markedly lower specificity (79.13%) and precision (81.10%). The AUC for PNN (0.926578) also exceeded the values of this parameter for the remaining classifiers (Figure 1). The averaged accuracy, sensitivity, specificity, precision, goodness index, and AUC value obtained for all applied computational intelligence methods and a linear regression model are presented in Table 4.  
Figure 2: J48 tree design.  
The values were obtained using a 10-fold cross-validation procedure [49]. The given dataset consisting of 230 samples was split into 10 folds, where each fold was used as a testing set at some point. In the first iteration, the first fold was used to test the model, and the rest were used to train the model. In the second iteration, the second fold was used as the testing set, and the rest served as the training set. This process was repeated until each fold of the 10 folds had been used as the testing set. Based on the obtained scores in every iteration, the mean value was calculated in order to assess the performance of the model.