## 1 Introduction
The process flow of these models, illustrated in Figure 1, encompasses the essential steps for prediction: data extraction, feature definition, data modeling, and subsequent validation. In intrapartum EFM, an ultrasound transducer is commonly used to externally monitor the fetal heart rate (FHR). The transducer comprises crystals that generate ultrasound waves by converting electrical energy through the piezoelectric effect. Placed on the mother's abdomen, the transducer produces a waveform that can be analyzed to derive the FHR [18,19]. Central monitoring systems have been developed to enable healthcare providers to monitor multiple fetal signals from various locations simultaneously. These systems typically consist of a computer equipped with specialized software and several external monitors that are connected to the computer. The computer screen displays the real-time continuous recording of the fetal heart rate (FHR) and uterine contractions. This allows healthcare providers to closely observe the fetal signals and detect any changes or abnormalities that may necessitate intervention. The signals can also be printed for further analysis [20]. Interpreting patterns in the fetal heart rate can be complex, and misinterpretation can result in unnecessary interventions such as cesarean sections and operative deliveries [16]. Machine learning algorithms provide a wide range of methodologies to analyze FHR data. [PERSON] et al. conducted an extensive review in this context [21]. They distinguished between studies that offer a complete computer-aided diagnosis system [15; 22; 23; 24] and those that utilize the signal to enhance fetal state detection [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. Regarding the algorithms utilized, decision trees [16; 30], random forest [17; 29], adaptive boosting [16], support vector machines [21; 23; 26; 27; 28; 30], artificial neural networks [17; 21; 26; 30; 34; 35; 36; 37], K-nearest neighbor [21; 26], convolutional neural networks [24; 29; 31; 32; 34; 38], fuzzy approach [28; 37], Naive Bayes [28], deep Gaussian processes [33], and deep-ANFIS models [37] have been considered to enhance acidemia prediction. Overall, machine learning algorithms offer promising avenues for improving the accuracy of fetal acidemia prediction, based on the FHR signal or the features extracted from it. Another crucial aspect in constructing acidemia prediction models is the time window during which the FHR signal is analyzed. Traditionally, models have considered time periods ranging from 30 to 60 min. In this study, we hypothesize that the closer the recording is taken to delivery, the clearer the characteristics associated with hypoxia will be. Therefore, we focus on the variables obtained from the signal within the last 30 min before delivery, as well as during the last deceleration. Utilizing these extracted variables, we employ various machine learning techniques, including logistic regression, classification trees, random forest, and deep neural networks. Going beyond previous research, our study examines the practical benefits of our model in terms of minimizing misclassified cases of acidosis and avoiding unnecessary cesarean sections at different threshold points. This aspect, often overlooked in previous studies, holds significant importance as it serves as the primary objective in this type of investigation.