## 5 Discussion  
### Performance of RRI-Based Deep Learning Model  
The RRI model proposed in this study exhibited 45% BAC, 0.57 QWK, 60% accuracy, 0.46 precision, and 0.45 F1-score in classifying the four age groups using hybrid loss with ADASYN. In addition, the BAC and F1 achieved by the RRI model were the highest among all the data types and methods. Comparisons among the three models suggest that the RRI model can perform similarly to the HRV and combined models, demonstrating the potential of the RRI-based deep learning model for automated age prediction. However, our models showed limited efficacy in accurately predicting all age groups, indicating the need for significant improvement before they can be considered reliable age prediction methods.  
The RRI model could not classify the minority classes. Although the bias to Group 1 was considerably reduced using hybrid loss with ADASYN, the recall obtained from the RRI model was 73%, 31%, 24%, and 50% for Groups 1, 2, 3, and 4, respectively (Figure 7). One possible cause of the inaccurate prediction of minority classes is data imbalance. Group 1 contained 63% of the total samples, which was approximately five to six times more than the other minority groups (Table 1). We used ADASYN oversampling to balance  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **HRV** & **RRI** & **Combined** \\ \hline Cohen’s kappa & 0.770 & 0.678 & 0.737 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Cohen’s kappa representing agreement between predictions of two distinct samples from the same subject.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method (RRI Model)** & **BAC (\%)** & **QWK** & **Accuracy (\%)** & **Precision** & **F1** \\ \hline Classification loss & 37.5 \(\pm\) 0.343 & 0.492 \(\pm\) 0.010 & 68.6 \(\pm\) 0.482 & 0.381 \(\pm\) 0.019 & 0.343 \(\pm\) 0.004 \\ Classification loss + ADASYN & 42.7 \(\pm\) 0.555 & 0.555 \(\pm\) 0.010 & 65.0 \(\pm\) 0.496 & 0.451 \(\pm\) 0.008 & 0.426 \(\pm\) 0.006 \\ Hybrid loss & 38.2 \(\pm\) 0.364 & 0.482 \(\pm\) 0.011 & 67.8 \(\pm\) 0.498 & 0.303 \(\pm\) 0.004 & 0.337 \(\pm\) 0.003 \\ Hybrid loss + ADASYN & 43.4 \(\pm\) 0.569 & 0.594 \(\pm\) 0.008 & 60.4 \(\pm\) 0.561 & 0.434 \(\pm\) 0.005 & 0.429 \(\pm\) 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Mean and 95% CI of the prediction performance of the RRI model using different sample windows.  
the training data for each class, although the effect of ADASYN alone on reducing bias was limited. The prediction of minority classes was further improved when ADASYN was combined with hybrid loss, particularly in the RRI model. Previous studies have suggested that oversampling improves the prediction of minority classes in DNNs [52; 53], although a sampling method may not be sufficient to solve the imbalance problem in all cases [72].  
Furthermore, we developed models for predicting an age class instead of a numeric value of age, because the subjects' age data were anonymized and generalized to age groups in the dataset (see Materials and Methods section for more details). The loss of individual age data increased the difficulty of classifying subjects who were near the group boundaries. For example, 29-year-old and 30-year-old subjects have only a one-year age difference, but they are labeled as Groups 1 and 2, separated by a decade gap. Near-boundary samples can show a higher probability of being misclassified into adjacent age groups. In the RRI model using hybrid loss with ADASYN, 54% of the actual Group 2 samples were misclassified as Group 1, and 23% of Group 3 samples were misclassified as Group 2. We used the MSE to develop the hybrid loss function to improve the learning ordinal attributes of the age group; however, the hybrid loss cannot address issues from near-boundary samples unless actual age values are provided.