Prediction of the Mode of Delivery Using Artificial Intelligence Algorithms.

Background and objective: Mode of delivery is one of the issues that most concerns obstetricians. The caesarean section rate has increased progressively in recent years, exceeding the limit recommended by health institutions. Obstetricians generally lack the necessary technology to help them decide whether a caesarean delivery is appropriate based on antepartum and intrapartum conditions. Methods: In this study, we have tested the suitability of using three popular artificial intelligence algo-rithms, Support Vector Machines, Multilayer Perceptron and, Random Forest, to develop a clinical deci-sion support system for the prediction of the mode of delivery according to three categories: caesarean section, euthocic vaginal delivery and, instrumental vaginal delivery. For this purpose, we used a com-prehensive clinical database consisting of 25,038 records with 48 attributes of women who attended to give birth at the Service of Obstetrics and Gynaecology of the University Clinical Hospital "Virgen de la Arrixaca" in the Murcia Region (Spain) from January of 2016 to January 2019. Women involved were pa-tients with singleton pregnancies who attended to the emergency room on active labour or undergoing a planned induction of labour for medical reasons. Results: The three implemented algorithms showed a similar performance, all of them reaching an accu-racy equal to or above 90% in the classification between caesarean and vaginal deliveries and somewhat lower, around 87% between instrumental and euthocic. Conclusions: The results validate the use of these algorithms to build a clinical decision system to help gynaecologists to predict the mode of delivery.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )