## 4 Results
### Comparison between Combined, HRV, and RRI Models  
We compared the performance of the combined model with the HRV and RRI models to test whether the prediction was improved by combining the two data types. Tables 9 and 10 list the differences in performance metrics between the combined, HRV, and RRI models that used the same method. The differences were calculated by subtracting the HRV or RRI model's value from the value of the combined model. We performed a two-sample t-test to compare the metrics between the two models statistically. When using hybrid loss with ADASYN, there was no significant difference in BAC, QWK, or F1-score between the combined and HRV models (Table 9). The BAC differed significantly in three methods, with the HRV model outperforming the combined model in two cases (Table 9). The QWK differed significantly in three methods, with the combined model outperforming the HRV model in two cases (Table 9). The accuracy differed significantly in three methods, with the combined model outperforming the HRV model in all three cases (Table 9). The precision differed significantly in four methods, with the HRV model outperforming the combined model in three cases (Table 9). The F1-score differed significantly in two methods, with the combined model outperforming the HRV model when using hybrid loss without ADASYN. When using hybrid loss with ADASYN, the RRI model showed significantly higher BAC, precision, and F1-score and significantly lower QWK and accuracy than the combined model (Table 10). The BAC differed significantly in two methods, with the RRI model outperforming the combined model when using hybrid loss with ADASYN (Table 10). The QWK differed significantly in three methods, with the combined model outperforming the RRI model in two cases (Table 10). The accuracy differed significantly in four methods, with the combined model outperforming the RRI model in three cases. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Method**} & **Difference** & \multirow{2}{*}{**\(\mathbf{p}\)-Value**} \\  & & **(RRI\(-\)HRV)** & \\ \hline \multirow{4}{*}{BAC (\%)} & Classification loss & \(-\)1.023 \(\pm\) 0.546 & 0.001 * \\  & Classification loss + ADASYN & \(-\)3.006 \(\pm\) 0.696 & \(<\)0.001 * \\  & Hybrid loss & 1.394 \(\pm\) 0.371 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & 1.266 \(\pm\) 0.761 & 0.003 * \\ \hline \multirow{4}{*}{QWK} & Classification loss & \(-\)0.019 \(\pm\) 0.012 & 0.018 * \\  & Classification loss + ADASYN & 0.024 \(\pm\) 0.010 & 0.001 \\  & Hybrid loss & 0.044 \(\pm\) 0.013 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & \(-\)0.002 \(\pm\) 0.010 & 0.053 \\ \hline \multirow{4}{*}{Accuracy (\%)} & Classification loss & 1.573 \(\pm\) 0.377 & \(<\)0.001 * \\  & Classification loss + ADASYN & 2.083 \(\pm\) 0.522 & \(<\)0.001 * \\  & Hybrid loss & 0.210 \(\pm\) 0.303 & 0.544 \\  & Hybrid loss + ADASYN & \(-\)1.547\(\pm\) 0.607 & \(<\)0.001 * \\ \hline \multirow{4}{*}{Precision} & Classification loss & 0.003 \(\pm\) 0.022 & 0.794 \\  & Classification loss + ADASYN & 0.009 \(\pm\) 0.012 & 0.155 \\  & Hybrid loss & \(-\)0.009 \(\pm\) 0.004 & 0.001 * \\  & Hybrid loss + ADASYN & 0.048 \(\pm\) 0.008 & \(<\)0.001 * \\ \hline \multirow{4}{*}{F1} & Classification loss & \(-\)0.031 \(\pm\) 0.006 & \(<\)0.001 * \\  & Classification loss + ADASYN & \(-\)0.020 \(\pm\) 0.008 & \(<\)0.001 * \\ \cline{1-1}  & Hybrid loss & 0.003 \(\pm\) 0.003 & 0.181 \\ \cline{1-1}  & Hybrid loss + ADASYN & 0.030 \(\pm\) 0.007 & \(<\)0.001 * \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Mean and 95% CI of difference in performance metrics between the RRI and HRV models. The differences were calculated by subtracting the value of an HRV model from that of an RRI model. A two-sample \(t\)-test was performed to compare the metrics from the two models (* \(p<\) 0.05). Specifically, the RRI model outperformed the combined model in terms of BAC and F1-score when using hybrid loss with ADASYN, suggesting better performance in addressing class imbalance.