Prediction of risk for cesarean delivery in term nulliparas: a comparison of neural network and multiple logistic regression models

Abstract

Objective: We sought to develop a neural network (NN) to predict the risk for cesarean delivery (CD) in term nulliparas.

Study design: Using software (BrainMaker for Windows, Version 3.0; California Scientific Software, Nevada City, CA), we trained an NN with 225 patients obtained by chart review and included for nulliparity, singleton vertex > 36 weeks' gestation, and reassuring fetal heart rate on admission. Training inputs included several maternal and fetal clinical variables. Two logistic regression (LR) models using 225 and 600 patients (LR225 and LR600, respectively) were developed. The NN and LR models were tested for prediction of CD in a set of 100 patients not used for development.

Results: The NN, LR225, and LR600 correctly predicted 53%, 26%, and 32% of the patients with CD and 88%, 95%, and 95% of the patients with vaginal delivery, respectively.

Conclusion: Compared with LRs, the NN was slightly better in predicting CD and was similar for predicting vaginal delivery in nulliparas with term singletons.