Prediction of the mode of delivery using artificial intelligence algorithms. (June 2022)

• Record Type: Journal Article
• Authors: De Ramón Fernández, Alberto; Ruiz Fernández, Daniel; Prieto Sánchez, María Teresa
• Is Part Of: Computer methods and programs in biomedicine. Volume 219(2022)
• Publication Date: 2022-06
• Subjects: Gynaecology -- Mode of delivery prediction -- CDSS -- Artificial intelligence -- Machine learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106740
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Mode of delivery worries mothers and doctors. A CDSS can help doctors to predict the mode of delivery. MLP, SVM and Random Forest are used as classification algorithms. Accuracy higher than 95% is reached. Abstract: 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 algorithms, Support Vector Machines, Multilayer Perceptron and, Random Forest, to develop a clinical decision 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 comprehensive 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 patients 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 accuracy 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.