## 1 Introduction  
Approximately 11% of all babies are born preterm, of which 15.6% are born before 32 weeks of gestation and are admitted to the neonatal intensive care unit (NICU). These preterm infants are extremely vulnerable, with a high incidence of mortality and morbidity, especially due to late onset sepsis (LOS), which mostly occurs beyond the third day of life [1]. Prevention and early detection of this life-threatening bloodstream infection is of the utmost importance, particularly because this condition is not only associated with long-term morbidity and mortality, but it also results in increased costs residing from prolonged hospitalization [2]. Directly after birth, preterm infants are continuously monitored and often need intensive and prolonged treatment to support their vital functions. However, early signs of infectious episodes are non-specific and lead to both over- and undertreatment with antibiotics, which may disrupt normal microbiome development and can lead to antibiotic resistance [2]. Moreover, even when there is a clear indication of LOS and blood cultures are consequently taken, it can take up to 48 h before a result is available, which means that valuable time is lost when treating sepsis. Lastly, the results of blood cultures are prone to false positives due to contamination [3,4]. Therefore, a real-time non-invasive continuous monitoring system using an algorithm detecting a decrease in heart rate variability might be a useful early warning system for LOS in preterm infants. Currently, some hospitals are using such systems as a non-invasive tool to identify several threatening conditions at an early stage. One example of such a system is the heart rate observation system (HeRO). Among other conditions (e.g., necrotizing enterocolitis, meningitis, respiratory decomposition, brain pathology, and death), this system is capable of identifying several LOS prior to patient deterioration [2]. According to studies on the clinical effectiveness of this system, it has been shown that the HeRO monitor effectuated a significant decrease in the amount of mortality (2%) [5] and length of stay (3.2 days) [6]. Although these monitors have been shown to be quite effective, the algorithm is based on a linear model (i.e., logistic regression) [2], which might ignore possible non-linearities that are present in the data. Because of the complexity of LOS, it has been hypothesized that capturing these non-linearities with more advanced models might improve the LOS detection performance of the algorithm. Moreover, these monitors only use features derived from heart rate, and do not use other vital parameters such as perfusion index and oxygen saturation. Interestingly, a recent study using various ML methods found that logistic regression is quite robust and, in some cases, even superior with respect to some non-linear methods when predicting LOS [7,8]. However, the current literature is very scarce with respect to the assessment of non-linear methods when predicting LOS. For instance, adaptive boosting has not been assessed yet in the context of early LOS prediction, only in EOS (i.e., early onset sepsis) prediction [9]. Moreover, most studies have focused on ML-based methods to predict sepsis in adults and not in neonates [10]. Thus, although previous research has pointed out that mainly linear machine learning models are superior when predicting LOS, we hypothesized that some non-linear models could be potentially valuable when predicting non-linear dependencies in the data. Lastly, previous studies have not used all of the parameters that are available in NICU monitoring, as features derived from heart rate and respiratory variability are considered to be most valuable when predicting LOS [7,11,12,13,14]. To the best of our knowledge, perfusion index (PFI) and oxygen saturation (SpO2) have not yet been assessed in the context of LOS prediction.