Improving delivery mode forecasting with deep neural network: a time-based convolutional network strategy

Abstract

The caesarean section is one of the most frequently performed surgical procedures worldwide, with profound implications for maternal and neonatal health. Accurate prediction of delivery mode is essential for guiding clinical decisions, minimizing unnecessary surgical interventions, and improving patient outcomes. This study introduces a deep neural learning technique based on a temporal convolutional neural network (DNLTC) to classify delivery type—caesarean section versus normal vaginal delivery using maternal and obstetric data. The proposed model was evaluated against traditional machine learning (ML) approaches, including artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT). Experimental results show that the DNLTC achieved the highest overall accuracy (85%), surpassing ANN (80%), SVM (68.8%), and DT (65%). TCNN also demonstrated strong clinical reliability, with a sensitivity of 94%, specificity of 91%, and a perfect F1-score of 100%. These findings highlight the advantages of incorporating temporal feature learning into delivery mode prediction, enabling the detection of subtle, sequential patterns that conventional models may overlook. By providing more accurate and robust predictions, the proposed framework can support obstetricians in making timely, evidence-based decisions, ultimately enhancing maternal and newborn health outcomes.