Development and validation of a machine learning model for prediction of cephalic dystocia

Early detection of cephalic dystocia is challenging, and current clinical assessment tools are limited. Machine learning offers unique advantages, enabling the generation of predictive models using various types of clinical data. Our model aims to integrate objective ultrasound data with psychological and sociological characteristics and obstetric treatment data to predict the individual probability of cephalic dystocia in pregnant women.

We collected data from 302 pregnant women who underwent examinations and deliveries at Southern Medical University's Nanfang Hospital from January 2022 to December 2023. We utilized basic patient characteristics, foetal ultrasound parameters, maternal anthropometric data, maternal psychological measurements, and obstetric medical records to train and test the machine learning models. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select predictive factors, followed by the development of logistic regression, decision tree, and random forest machine learning models. The precision, accuracy, recall, F1-score, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve were used to evaluate the performance of the models.

Among the three machine learning models, the LASSO-based logistic regression model demonstrated the best predictive performance, with an AUC value of 0.914. We found that maternal ischial spine diameter, fetal head circumference-to-maternal height ratio, artificial rupture of membranes, childbirth self-efficacy, and other 11 variables were predictive factors for cephalic dystocia.

This study applied a LASSO-based logistic regression prediction model for cephalic dystocia. The model demonstrated good predictive performance and can assist in selecting the appropriate mode of delivery.