## 2 Material and Methods
### Statistical Analysis
#### 2.3.2 Model Validation  
For the validation, the models were evaluated through calibration, discrimination, and clinical utility. The calibration curve was used to visually assess the agreement between predicted values and actual outcomes, with a diagonal line indicating perfect calibration. Additionally, two informative parameters, 'intercept' (calibration-in-the-large) and'slope,' were examined. The 'intercept' quantifies the disparity between the average predictions and average outcomes, while the'slope' indicates the average influence of predictions on the outcome [40].  
The discrimination was assessed using the receiver operating characteristic (ROC) curve. The ROC curve is a plot of pairs of sensitivity (true positive rate, \(y\)-axis) versus 1-specificity (false positive rate, \(x\)-axis) obtained for different cut-off values of acidemia probability. The area under the ROC curve (AUC) is a parameter that summarizes the discrimination ability of a predictive model. The AUC measures the probability that the model assigns a higher probability of being acidotic to an actual acidotic case compared to a non-acidotic case. It has a range of 0 to 1, where 0.5 indicates a random model, 0.7 is considered acceptable, 0.8 suggests a good model, 0.9 indicates an excellent model,  
Figure 4: Calculation of slope: The slope is determined by calculating the ratio between the amplitude of the fall of the deceleration in bpm (represented by the downward blue line) and the duration in seconds of the fall of the descending limb of the deceleration (measured from the basal level to the nadir or trough) (shown as the horizontal blue line).  
and 1 represents perfect discrimination. The 95% confidence intervals for the AUC were calculated using DeLong estimation [41].  
Furthermore, we investigated and compared the specificities for various sensitivities thresholds (0.8, 0.85, 0.9, 0.95) using a proportion test.  
Additionally, we assessed the practical applicability of the developed machine learning models by evaluating their clinical usefulness. This evaluation involved treating the prediction models as dichotomous classification models, using a specific cutoff point to distinguish between positive (1) and negative (0) individuals above or below the threshold. To evaluate the clinical utility, we employed the clinical utility curve [42]. This curve utilizes the threshold probability on the \(x\)-axis to identify neonates as acidotic, while the \(y\)-axis represents the percentage of two distinct measures. The first measure indicates the percentage of acidotic infants that were incorrectly classified below the chosen cutoff point, and the second measure represents the number of infants falling below the cutoff point. By examining this curve for different cutoff points, we can determine the percentage of misclassified acidotic fetuses and the fetuses with a very low risk of acidemia who can be spared unnecessary cesarean sections due to fetal well-being concerns. These parameters are crucial in clinical practice.  
The rms and pROC R libraries and the CUC R code function were used for validation purposes.