## 4 Results
### Confusion Matrices  
The confusion matrices for the proposed models are shown in Figures 6-8, which illustrate the effect of class imbalance on the prediction. All classifiers were skewed to choose Group 1 as a prediction, regardless of the ground truth (Figures 6-8). In addition, recall showed significant differences among the classes. Group 1 always showed the highest recall among the age groups in each confusion matrix, which was between 73 and 97% (Figures 6-8). Group 4 always showed the second highest recall among the age groups in each confusion matrix, which was between 44 and 65% (Figures 6-8). By contrast, Groups 2 and 3 showed a substantially lower recall, which was between 0 and 31% and 0 and 24%, respectively, compared to Groups 1 and 4 (Figures 6-8). The confusion matrices indicate that using hybrid loss with ADASYN increased the recall of Groups 2 or 3, although it decreased the recall of Group 1 in all data types (Figures 6-8). However, applying either hybrid loss or ADASYN led to mixed outcomes, affecting the recall of Groups 2 and 3. These results were consistent with the trends observed in the performance metrics (Section 4.2), which showed that using hybrid loss with ADASYN increased the BAC, QWK, or F1-score but decreased the accuracy. The confusion matrices demonstrated that predicting Groups 2 and 3 was not successful and that accuracy should be interpreted with caution if there is an imbalance in multiclass datasets. In all models, high accuracy was achieved by simply choosing Group 1 as a prediction. Using hybrid loss with ADASYN resulted in a decrease in accuracy compared with the base method, although the prediction of Groups 2 and 3 was improved. This is because the recall of Group 1 was decreased using hybrid loss with ADASYN, which considerably affected the accuracy. The effect of a decrease in Group 1 recall on the overall accuracy was amplified because of the large sample size of Group 1. These results suggest that BAC, QWK, and F1 should be considered as more important metrics than accuracy to prevent the misinterpretation of models' performance. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline  & & \multicolumn{2}{c}{**HRV**} & \multicolumn{3}{c}{**RRI**} & \multicolumn{3}{c}{**Combined**} \\ \hline
**Method** & **Group** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** \\ \hline \multirow{3}{*}{Classification} & 1 & 0.928 & 0.734 & 0.819 & 0.961 & 0.719 & 0.822 & 0.931 & 0.718 & 0.810 \\  & 2 & 0.021 & 0.132 & 0.036 & 0.013 & 0.332 & 0.024 & 0.035 & 0.169 & 0.057 \\ loss & 3 & 0.112 & 0.218 & 0.145 & 0.006 & 0.057 & 0.011 & 0.101 & 0.226 & 0.136 \\  & 4 & 0.496 & 0.532 & 0.510 & 0.538 & 0.522 & 0.528 & 0.443 & 0.555 & 0.489 \\ \hline \multirow{3}{*}{Classification} & 1 & 0.789 & 0.787 & 0.787 & 0.861 & 0.766 & 0.810 & 0.905 & 0.752 & 0.821 \\  & 2 & 0.209 & 0.262 & 0.230 & 0.143 & 0.192 & 0.163 & 0.124 & 0.239 & 0.161 \\ loss + ADASYN & 3 & 0.166 & 0.232 & 0.190 & 0.113 & 0.321 & 0.164 & 0.150 & 0.237 & 0.181 \\  & 4 & 0.627 & 0.464 & 0.531 & 0.553 & 0.501 & 0.523 & 0.478 & 0.607 & 0.532 \\ \hline \multirow{3}{*}{Hybrid loss} & 1 & 0.965 & 0.702 & 0.812 & 0.953 & 0.719 & 0.820 & 0.933 & 0.745 & 0.828 \\  & 2 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.043 & 0.333 & 0.074 \\  & 3 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.120 & 0.301 & 0.168 \\  & 4 & 0.546 & 0.591 & 0.565 & 0.614 & 0.537 & 0.571 & 0.664 & 0.606 & 0.631 \\ \hline \multirow{3}{*}{Hybrid loss + ADASYN} & 1 & 0.760 & 0.815 & 0.786 & 0.730 & 0.798 & 0.762 & 0.833 & 0.788 & 0.809 \\  & 2 & 0.226 & 0.213 & 0.217 & 0.318 & 0.213 & 0.253 & 0.187 & 0.219 & 0.200 \\ \cline{1-1}  & 3 & 0.097 & 0.116 & 0.104 & 0.239 & 0.221 & 0.227 & 0.077 & 0.177 & 0.105 \\ \cline{1-1}  & 4 & 0.657 & 0.488 & 0.558 & 0.504 & 0.591 & 0.541 & 0.621 & 0.504 & 0.553 \\ \hline \end{tabular}
\end{table} 
Table 7: Mean prediction performance for each age group based on the data type and method.