## 7 Conclusions  
In this study, we aimed to assess various linear and non-linear ML methods when predicting LOS. In addition, we assessed the added value of various vital parameters. In contrast with the findings in the literature, the results indicate that non-linear methods displayed a superior compared with the linear methods. Interestingly, we found that random forest performed best (AUROC: 0.973), without any dimension reduction (i.e., PCA) applied. However, when taking the trade-off between training time and predictive performance into account, we found that the K-nearest neighbors model was preferred when compared with the random forest. In line with the literature [37], we found that training the models on HRV features yielded the best results and adding RF (respiratory) features to the HRV features ensured the greatest improvement in terms of AUROC score for most models. Conceptualization, A.G.G. ; methodology, A.G.G. and C.R.R. ; software, A.G.G. ; validation, A.G.G., C.R.R. and W.O. ; formal analysis, A.G.G. and C.R.R. ; investigation, A.G.G. ; resources, A.G.G. and W.O. ; data curation, A.G.G. and C.R.R. ; writing--original draft preparation, A.G.G. ; writing--review and editing, A.G.G. and C.R.R. ; visualization, A.G.G. ; supervision, C.R.R. and W.O. ; project administration, C.R.R. and W.O. ; funding acquisition, W.O. All authors have read and agreed to the published version of the manuscript. This work was supported by the University of Amsterdam. **Institutional Review Board Statement:** Written informed consent for participant and publication of the study results was obtained from all parents. **Informed Consent Statement:** Written informed consent has been obtained from both parents of the patients to reuse the data for retrospective studies. **Data Availability Statement:** Upon a reasonable request, the corresponding author can offer a partial code for the study upon completion of all projects. **Conflicts of Interest:** The authors declare no conflict of interest. Figure A6: Cont. Figure A7: AUROC score for each combination of feature sets using a decreasing training window. Figure A8: AUROC score for each combination of feature sets using a decreasing training window, trained on the PCA dataset (\(\Delta\) PCA_Significant_Features). Figure A9: ROC plot of the models trained on the entire learning window, trained on the PCA dataset (\(\Delta\) PCA_Significant_Features) (i.e., \(t_{i}=-48\)). Dashed line: Curve of a random classifier (the diagonal line). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Unit** & **Interpretation** \\ \hline HRV & & \\ \hline Time domain & & \\ mean\_nni & ms & Mean of nni \\ sd\_nn & ms & Standard deviation of nni \\ sd\_diff\_nn & ms & Standard deviation of differences between adjacent nn-intervals \\ rmssd & ms & The square root of the mean of the sum of the squares of differences between adjacent nni \\ max\_nn & ms & Intervals \\ min\_nn & ms & Maximum of nni \\ min\_nn & ms & Minimum of nni \\ nni\_50 & ms & Number of interval differences of successive nn-intervals greater than 50 ms \\ pnni\_50 & \% & The proportion derived by dividing nni-50 by the total number of nn-intervals \\ mni\_20 & ms & Number of interval differences of successive nn-intervals greater than 20 ms \\ pnni\_20 & \% & The proportion derived by dividing nni20 by the total number of nni. \\ range\_nni & ms & Difference between the maximum and minimum nn-interval. \\ cvsd & ms & Coefficient of variation of successive differences equal to the rmssd divided by mean\_nni. \\ cvnni & ms & Coefficient of variation equal to the ratio of sdnn divided by mean\_nni \\ Frequency domain & & \\ total\_power & ms’2 & Total power density spectral \\ \hline \hline \end{tabular}
\end{table} 
Table A1: Overview of features that are extracted from the vital parameters. HRV: heart rate variability; RF: respiratory frequency, PFL perfusion index, SpO2: oxygen saturation. **Table A1. **_Cont._