doi:10.3390/e24010068engEsteban-Escaño, JavierCastán, BertaCastán, SergioChóliz-Ezquerro, MartaAsensio, CésarLaliena, Antonio R.Sanz-Enguita, GerardoSanz, GerardoEsteban, Luis MarianoSavirón, RicardoMachine learning algorithm to predict acidemia using electronic fetal monitoring recording parametersART-2022-125594Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.2022http://zaguan.unizar.es/record/10960810.3390/e24010068http://zaguan.unizar.es/record/109608oai:zaguan.unizar.es:109608info:eu-repo/grantAgreement/ES/DGA/E46-20Rinfo:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00ENTROPY 24, 1 (2022), 68 [16 pp.]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess