Utilizing machine learning to predict unplanned cesarean delivery
Raanan Meyer, Boaz Weisz, Roni Eilenberg, Meytal Avgil Tsadok, Moshe Uziel, Eyal Sivan, Shali Mazaki‐Tovi, Abraham Tsur
Abstract
OBJECTIVE: To develop a comprehensive machine learning (ML) model predicting unplanned cesarean delivery (uCD) among singleton pregnancies based on features available at admission to labor. METHODS: A retrospective cohort study from a tertiary medical center. Women with singleton vertex pregnancy of 34 weeks or more admitted for vaginal delivery between March 2011 and May 2019 were included. The cohort was divided into training (80%) and validation (20%) data sets. A separate cohort between June 2019 and April 2021 served as a test data set. Features selection was performed using a Random Forest ML algorithm. RESULTS: The study population included 73 667 women, of which 4125 (6.33%) underwent uCD. The final model consisted of 13 features, based on prediction importance. The XGBoost model performed best with areas under the curve for the training, validation, and test data sets of 0.874, 0.839, and 0.840, respectively. The model showed a 65% positive predictive value for uCD among women in the 100th centile group, and a 99% or more negative predictive value in the less than 50th centile group. Positive and negative predictive values remained high among subgroups with high pretest probability of uCD. CONCLUSION: An ML model for the prediction of uCD provides clinically useful risk stratification that remains accurate across gestational weeks 34-42 and among clinical risk groups. The model may be clinically useful for physicians and women admitted for labor. SYNOPSIS: A machine learning model predicts unplanned cesarean delivery and can inform women's individualized decision making.