## 5 Conclusions  
By utilizing FHR recordings, we have successfully developed machine learning models for predicting acidemia. These models have demonstrated favorable accuracy, with an AUC ranging from 0.896 for the classification tree, 0.926 and 0.962 for the two- and one-hidden-layer neural networks, 0.968 for the logistic regression, to 0.971 for the random forest model in the validation dataset. Given the critical importance of accurately detecting acidosis, which is related to fetal hypoxia, the best model can be considered the one that corresponds to a very high rate of acidemia detection. When aiming for a sensitivity of 90%, the specificities ranged from 0.783 for the two-hidden-layer neural network to 0.817 for the classification tree, 0.892 for the random forest, 0.916 for the one-hidden-layer neural network, to 0.879 for the logistic regression model. For a sensitivity of 95%, the specificity of the neural network decreased to 0.771, while the logistic regression model maintained the highest specificity value of 0.879. Therefore, the logistic regression model can be considered the best and most robust model for predicting acidosis.  
We combined previously explored parameters, such as deceleration within the 30 min window, with new parameters derived from the last deceleration (slope, drop, duration, variation in duration, and drop), resulting in a highly accurate predictive model and a user-friendly nomogram for their use. These variables can be routinely extracted from the FHR signal, providing a practical tool for healthcare professionals. In clinical practice, the nomogram provided by the logistic regression model can be implemented with a cutoff point of 31% probability for acidemia. This threshold minimizes the occurrence of missed acidemia cases (5%) while effectively preventing unnecessary cesarean sections in 51% of cases.  
Overall, our study demonstrates the effectiveness of machine learning models in predicting acidemia using FHR recordings. The logistic regression model, along with the developed nomogram, offers a valuable and user-friendly tool for healthcare providers to make informed decisions and improve fetal health outcomes.  
Conceptualization, L.M.E., S.C. and R.S.-C.; methodology, L.M.E., B.C. and J.E.-E.; software, J.E.-E., G.S.-E. and A.R.L.; validation, L.M.E., B.C., A.C.L.-M. and S.C.; formal analysis, L.M.E. and R.S.-C.; investigation, S.C.; resources, L.M.E.; data curation, M.C.-E.; writing--original draft preparation, L.M.E., B.C. and R.S.-C.; writing--review and editing, L.M.E., B.C., J.E.-E., M.C.-E., A.C.L.-M., A.R.L., G.S.-E., S.C. and R.S.-C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Government of Aragon, grant number T69_23D and Ministerio de Ciencia e Innovacion, MCIN/ AEI/10.13039/501100011033, grant number PID2020-116873 GB-I00.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Clinical Research Ethics Committee of Aragon (CEICA, PI 21/495).  
Patient consent was waived due to the retrospective character of the study.  
Data availability StatementData are available upon request.  
The authors declare no conflict of interest.