## 4 Discussion
Studies have shown that the total deceleration area and reperfusion time are superior predictors of fetal acidemia compared to the ACOG category III classification when considered as independent parameters. However, to achieve the best prediction, these parameters may need to be combined with other EFM, fetal, or maternal variables. This raises doubts about the validity of the current category system in existing EFM guidelines. Therefore, it is necessary to reevaluate and question the relevance and effectiveness of the current category system in EFM guidelines. Alternative approaches, such as incorporating total deceleration area and reperfusion time along with other relevant variables, should be explored to improve the prediction of fetal acidemia. Experimental studies conducted on term sheep fetuses have confirmed that extending the series of cordal occlusions leads to an increase in the slope of decelerations. This effect is more pronounced and observed more frequently in cases of severe acidosis [11]. The advancement of data extraction and monitoring devices necessitates the development of new software for analyzing fetal heart rate (FHR) data. With the digitalization of the FHR signal, it becomes feasible to process it using convolutional neural networks (CNNs) or more complex encoder-decoder deep learning architectures for acidemia prediction. In the work by [PERSON] [38], the MKNet model is introduced, which utilizes a CNN and achieves an impressive AUC value of 0.95. The author suggests its application in real-time fetal health monitoring using portable devices. Similarly, [PERSON] [16] also employs a CNN approach and achieves an AUC above 0.95 through a 10-fold cross-validation procedure for prediction purposes. Computer-aided diagnosis systems have shown lower predictive ability; [PERSON] [15] found a sensitivity of 76.83% and specificity of 78.27%, although [PERSON] [26] demonstrated an AUC of 0.96 for detecting cardiac anomalies. Despite the high accuracy demonstrated by some models, there is a lack of research on their clinical utility. An alternative approach involves extracting variables from the fetal heart rate (FHR) signal and incorporating them into binary classification models for acidemia prediction. In our analysis, we trained logistic regression, classification trees, random forest, and neural networks using readily available FHR features as predictor variables from the electronic fetal monitoring (EFM) recordings. Among these models, the random forest algorithm demonstrated the highest performance in terms of AUC. The random forest model, known for its additive nature, provides robust predictions by combining multiple trees constructed with diverse sets of data and variables. In our study, the optimal model was achieved using 300 trees, with each tree exploring the predictive capacity of variables on different data samples. To prevent overfitting, we limited the number of cases at a terminal node to a maximum of three in each tree. Although the AUC is the most commonly used parameter to validate a predictive model, in practice, we need to correctly classify a high percentage of acidemia cases. Therefore, we are interested in the points on the ROC curve that correspond to high sensitivity. In this case, logistic regression was the best model, only failing to classify 5% of acidemia cases, achieving a specificity of 87.9%. However, our models demonstrated good accuracy overall. In terms of calibration analysis, logistic regression and random forest models showed well-distributed probabilities of acidemia across a wide range of values. In contrast, neural networks exhibited probabilities concentrated near 0 and 1, indicating overfitting. This concentration made it difficult to determine a suitable threshold probability for distinguishing acidotic and non-acidotic cases. Logistic regression and random forest models, being more robust, allowed for a detailed analysis of the trade-offs between misclassifications and avoided cesarean sections.