A predictive model for birth delivery mode utilizing shape characteristics of uterine contractions from electrohysterographic signals and obstetric parameters

• The test results showed that the simple random forest classifier that uses a combination of EHG topological features and obstetric factors as inputs showed a good performance with very high accuracy. • Best result achieved from the medial axis of the uterus. • We show a contextualized EHG-based method that can help medical staff decide if a birth should end with a C-section or not. • If you want to give birth naturally, our plan also checks to see if you might need an induction. Birth delivery outcomes have a substantial impact on maternal and neonatal health. The objective of this work is to develop a predictive model that can identify the method of delivery during active labor, specifically by identifying contraction bursts, so that physicians can make decisions more quickly. We trained and assessed our model using the Icelandic 16-electrode EHG Database, comprising 122 abdominal EHG recordings from 45 pregnant women (collected in Iceland between 2008 and 2010), including both third-trimester prenatal and labor recordings. Contractions were detected using the examination of zero-crossing rate (ZCR) and root mean square (RMS) of uterine electrohysterographic (EHG) data. By applying the discrete Fourier transform (DFT) to each contraction burst, we obtained geometric features. These, in conjunction with seven standardized obstetric parameters, served as inputs to a Random Forest (RF) classifier. 4 channels on the upper left of the uterus from the 16-channel EHG database showed the best consistency index (average CCI> 90%). The proposed model attained an accuracy of 99% (95% CI: 0.93–1.00) in distinguishing between cesarean and vaginal deliveries, and an accuracy of 94% (95% CI: 0.91–1.00) in differentiating spontaneous from induced vaginal deliveries. Analysis of feature importance indicated that shape-based UC features exhibited greater predictive power than certain conventional obstetric variables. The combination of UC’s geometry with obstetric data creates a robust, interpretable, and non-invasive framework for predicting delivery mode. The results suggest that this method may serve as an effective clinical decision-support tool to reduce unnecessary cesarean sections and improve delivery planning.