## 4 Discussion  
In this study, we performed a comparative analysis of different machine learning techniques to predict acidemia using fetal heart rate features obtained from continuous electronic fetal monitoring during the last 30 min of the intrapartum period. Our predictor variables included reperfusion time and deceleration time measured over the entire 30 min period. Furthermore, we examined parameters of the last deceleration and their changes within the 30 min timeframe. The slope of the last deceleration was found to be the most predictive parameter, which has previously been studied in animal models. Regarding FHR parameters related to acidosis, [PERSON] et al. (1971) ([PERSON] et al. (1971) ([PERSON] et al. (1971)) demonstrated an inverse correlation between deceleration area, umbilical pH, and Apgar score at 1 min. [PERSON] et al. (1975) ([PERSON] et al. (1971)) found a correlation between deceleration area and fetal pH values. [PERSON] et al. (2013) ([PERSON] et al. (1971)) established a correlation between the area of fetal bradycardia  
Figure 17: Clinical utility curves. Upper panel: Logistic regression (**left**); classification tree (**right**). Center panel: random forest (**left**); neural network with one hidden layer (**right**). Lower panel: Two-hidden-layer neural network. and the timing of acidemia, although their results were based on a small sample size of thirty acidemic infants. [PERSON] et al. (2011) [48] introduced the \"sixty rule\", which stated that decelerations with a depth below 60 bpm and lasting more than 60 s were more discriminatory for metabolic acidemia. [PERSON] et al. (2018) [8] reported that a persistent 10 min period of category III decelerations was significantly associated with acidemia. However, most records were classified as category II, and there was no association with neonatal acidemia, despite achieving a better AUC (0.64) than category III. [PERSON] et al. (2019) [49] compared computerized systems and reported that deceleration area was the best parameter for predicting neonatal acidemia. Logistic regression has been commonly used as a machine learning technique to combine FHR parameters. [PERSON] et al. (2018) [7] found that ACOG parameters such as minimal variability, the total number of late decelerations, prolonged decelerations, and total deceleration area showed high discrimination power. Combining deceleration area with maternal-fetal characteristics, they achieved a good discrimination ability of 0.83. [PERSON] et al. (2018) [8] obtained similar results, where a combination of total deceleration area, ever tachycardia, and ever moderate variability resulted in an AUC of 0.77. [PERSON] et al. (2022) [9] analyzed the total reperfusion time or inter-deceleration time, which is crucial in assessing fetal oxygenation. They combined this parameter with the number of decelerations, the number of decelerations greater than 60 s, the number of decelerations greater than 60 beats per minute, the number of recurrent decelerations greater than 60 beats per minute, the minimum depth of beats per minute, maternal parity, and fetal weight that is large for gestational age. Although this model has higher accuracy with an AUC of 0.826, it includes specific variables that may limit its clinical use. In fact, they recommended using only the reperfusion time with a cutoff of 23.75 min in 30 min (sensitivity of 90%, negative predictive value of 89%). Understanding the normal fetal physiological compensation response is crucial for accurately interpreting features observed in electronic fetal monitoring (EFM). EFM should be viewed as a dynamic process rather than a static classification based on morphological features [50]. The effectiveness of electronic fetal monitoring (EFM) as a predictor of fetal acidemia has been the subject of criticism, and concerns have been raised regarding its medical and legal implications [51].