## 4 Results
### Comparison between Combined, HRV, and RRI Models
Considering all four methods, these results suggest that the combined model would show similar performance in age prediction compared to the HRV and RRI models. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Method**} & **Difference (Combined\(-\)RRI)** & \multirow{2}{*}{_p_-Value**} \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & Classification loss & \(-0.199\pm 0.536\) & 0.492 \\  & Classification loss + ADASYN & \(-0.331\pm 0.681\) & 0.377 \\  & Hybrid loss & 4.818\(\pm\) 0.446 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & \(-1.813\pm\) 0.660 & \(<\)0.001 * \\ \hline \multirow{4}{*}{QWK} & Classification loss & \(-0.021\pm 0.011\) & 0.005 * \\  & Classification loss + ADASYN & \(-0.001\pm 0.008\) & 0.858 \\  & Hybrid loss & 0.070 \(\pm\) 0.008 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & 0.021 \(\pm\) 0.009 & 0.003 * \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Classification loss & \(-2.080\pm 0.353\) & \(<\)0.001 * \\  & Classification loss + ADASYN & 1.870 \(\pm\) 0.441 & \(<\)0.001 * \\  & Hybrid loss & 1.900 \(\pm\) 0.292 & 0.002 * \\  & Hybrid loss + ADASYN & 4.963 \(\pm\) 0.528 & \(<\)0.001 * \\ \hline \multirow{4}{*}{Precision} & Classification loss & \(0.009\pm 0.023\) & 0.432 \\  & Classification loss + ADASYN & 0.014 \(\pm\) 0.012 & 0.028 * \\  & Hybrid loss & 0.182 \(\pm\) 0.015 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & \(-0.034\pm\) 0.009 & \(<\)0.001 * \\ \hline \multirow{4}{*}{F1} & Classification loss & \(0.027\pm 0.006\) & \(<\)0.001 * \\  & Classification loss + ADASYN & \(0.009\pm 0.008\) & 0.048 * \\ \cline{1-1}  & Hybrid loss & 0.078 \(\pm\) 0.006 & \(<\)0.001 * \\ \cline{1-1}  & Hybrid loss + ADASYN & \(-0.029\pm 0.007\) & \(<\)0.001 * \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Mean and 95% CI of difference in performance metrics between the combined and RRI models. The differences were calculated by subtracting the value of an RRI model from that of a combined model. A two-sample \(t\)-test was performed to compare the metrics from the two models (*_p_ < 0.05). \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Method**} & **Difference (Combined\(-\)HRV)** & \multirow{2}{*}{_p_-Value**} \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & Classification loss & \(-1.222\pm 0.425\) & \(<\)0.001 * \\  & Classification loss + ADASYN & \(-3.337\pm 0.692\) & \(<\)0.001 * \\  & Hybrid loss & 6.211 \(\pm\) 0.493 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & \(-0.547\pm 0.728\) & 0.174 \\ \hline \multirow{4}{*}{QWK} & Classification loss & \(-0.040\pm 0.008\) & \(<\)0.001 * \\  & Classification loss + ADASYN & \(0.022\pm 0.010\) & 0.002 * \\  & Hybrid loss & 0.114 \(\pm\) 0.012 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & \(0.007\pm 0.009\) & 0.331 \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Classification loss & \(-0.507\pm 0.297\) & 0.135 \\  & Classification loss + ADASYN & \(3.953\pm 0.510\) & \(<\)0.001 * \\  & Hybrid loss & 1.300 \(\pm\) 0.345 & \(<\)0.001 * \\  & Hybrid loss + ADASYN & \(3.417\pm 0.551\) & \(<\)0.001 * \\ \hline \multirow{4}{*}{Precision} & Classification loss & \(-0.001\pm 0.007\) & 0.036 * \\  & Classification loss + ADASYN & \(-0.005\pm 0.005\) & \(<\)0.001 * \\ \cline{1-1}  & Hybrid loss + ADASYN & \(-0.009\pm 0.005\) & 0.005 * \\ \hline \hline \multirow{4}{*}{F1} & Classification loss & \(-0.005\pm 0.005\) & 0.191 \\ \cline{1-1}  & Classification loss + ADASYN & \(-0.011\pm 0.008\) & 0.011 * \\ \cline{1-1}  & Hybrid loss & 0.081 \(\pm\) 0.006 & \(<\)0.001 * \\ \cline{1-1}  & Hybrid loss + ADASYN & \(0.001\pm 0.007\) & 0.841 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Mean and 95% CI of difference in performance metrics between the combined and HRV models. The differences were calculated by subtracting the value of an HRV model from that of a combined model. A two-sample \(t\)-test was performed to compare the metrics from the two models (*_p_ < 0.05).