## 4 Results
### Classifier Assessment
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Data** & **Method** & **BAC (\%)** & **QWK** & **Accuracy (\%)** & **Precision** & **F1** \\ \hline \multirow{4}{*}{HRV} & Classification loss & 39.0 \(\pm\) 0.459 & 0.514 \(\pm\) 0.011 & 67.1 \(\pm\) 0.477 & 0.404 \(\pm\) 0.008 & 0.378 \(\pm\) 0.005 \\  & Classification loss + ADASYN & **44.8 \(\pm\) 0.542** & 0.526 \(\pm\) 0.011 & 63.1 \(\pm\) 0.568 & 0.436 \(\pm\) 0.007 & 0.435 \(\pm\) 0.006 \\  & Hybrid loss & 37.8 \(\pm\) 0.399 & 0.475 \(\pm\) 0.013 & 68.8 \(\pm\) 0.486 & 0.323 \(\pm\) 0.004 & 0.344 \(\pm\) 0.004 \\  & Hybrid loss + ADASYN & 43.5 \(\pm\) 0.554 & 0.583 \(\pm\) 0.010 & 61.3 \(\pm\) 0.569 & 0.408 \(\pm\) 0.006 & 0.416 \(\pm\) 0.006 \\ \hline \multirow{4}{*}{RRI} & Classification loss & 37.9 \(\pm\) 0.390 & 0.495 \(\pm\) 0.011 & 68.6 \(\pm\) 0.493 & 0.407 \(\pm\) 0.022 & 0.346 \(\pm\) 0.004 \\  & Classification loss + ADASYN & 41.8 \(\pm\) 0.528 & 0.550 \(\pm\) 0.009 & 65.2 \(\pm\) 0.496 & 0.445 \(\pm\) 0.010 & 0.415 \(\pm\) 0.006 \\  & Hybrid loss & 39.2 \(\pm\) 0.317 & 0.519 \(\pm\) 0.009 & 69.0 \(\pm\) 0.467 & 0.314 \(\pm\) 0.003 & 0.348 \(\pm\) 0.003 \\  & Hybrid loss + ADASYN & **44.8 \(\pm\) 0.589** & 0.568 \(\pm\) 0.010 & 59.8 \(\pm\) 0.568 & 0.456 \(\pm\) 0.006 & **0.446 \(\pm\) 0.006** \\ \hline \multirow{4}{*}{Combined (RRI + HRV)} & Classification loss & 37.7 \(\pm\) 0.405 & 0.474 \(\pm\) 0.010 & 66.5 \(\pm\) 0.454 & 0.417 \(\pm\) 0.008 & 0.373 \(\pm\) 0.005 \\  & Classification loss + ADASYN & 41.4 \(\pm\) 0.505 & 0.549 \(\pm\) 0.009 & 67.1 \(\pm\) 0.495 & 0.459 \(\pm\) 0.007 & 0.424 \(\pm\) 0.006 \\ \cline{1-1}  & Hybrid loss & 44.0 \(\pm\) 0.489 & 0.589 \(\pm\) 0.010 & **70.1 \(\pm\) 0.511** & **0.496 \(\pm\) 0.014** & 0.425 \(\pm\) 0.006 \\ \cline{1-1}  & Hybrid loss + ADASYN & 42.9 \(\pm\) 0.552 & **0.590 \(\pm\) 0.010** & 64.7 \(\pm\) 0.553 & 0.422 \(\pm\) 0.008 & 0.417 \(\pm\) 0.006 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean and 95% CI of the prediction performance based on the data type and method. The bold entries indicate the best performance in terms of each evaluation metric. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Feature** & **Group 1** & **Group 2** & **Group 3** & **Group 4** & **\(p\)-Value** \\  & **(18–29, \(N\) = 696)** & **(30–39, \(N\) = 147)** & **(40–49, \(N\) = 100)** & **(\(\geq\)50, \(N\) = 150)** & **\(p\)-Value** \\ \hline \(\alpha\)1 & 0.99 \(\pm\) 0.25 & 1.07 \(\pm\) 0.30 & 1.14 \(\pm\) 0.28 & 1.08 \(\pm\) 0.31 & \(<\)0.001 * \\ \(\alpha\)2 & 0.72 \(\pm\) 0.17 & 0.73 \(\pm\) 0.18 & 0.85 \(\pm\) 0.23 & 0.91 \(\pm\) 0.24 & \(<\)0.001 * \\ Cordin & 1.43 \(\pm\) 0.17 & 1.36 \(\pm\) 0.18 & 1.32 \(\pm\) 0.22 & 1.26 \(\pm\) 0.23 & \(<\)0.001 * \\ SD1 & 43.14 \(\pm\) 32.82 & 46.65 \(\pm\) 91.15 & 34.68 \(\pm\) 55.53 & 38.59 \(\pm\) 86.54 & 0.311 \\ SD2 & 80.50 \(\pm\) 38.32 & 82.62 \(\pm\) 95.24 & 68.03 \(\pm\) 56.62 & 65.22 \(\pm\) 88.07 & 0.008 * \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._We considered the method using classification loss without ADASYN as the base method and examined whether applying hybrid loss or ADASYN would change the performance compared with the base method. Except for the accuracy, implementing hybrid loss or ADASYN improved the performance metrics in most cases. For example, the BAC, QWK, and F1-score were increased using hybrid loss or ADASYN for all three models, except when the HRV model used hybrid loss without ADASYN. Similarly, the precision was improved by using hybrid loss or ADASYN for all three models, except when the HRV and RRI models used hybrid loss without ADASYN. By contrast, the effect of hybrid loss and ADASYN on the accuracy showed the opposite trend. Using hybrid loss with ADASYN resulted in a 2-9% decrease in accuracy compared to the base method. Implementing classification loss with ADASYN also led to a decrease in accuracy for the HRV and RRI models. The use of hybrid loss without ADASYN improved the accuracy for all data types.