Vaginal birth after cesarean section prediction model for Jordanian population

Abstract

The rate of cesarean section has increased significantly worldwide, creating a group of women with one lower segment cesarean section concerned about the mode of delivery in their future pregnancies. This group of mothers will face a complex discussion because the likelihood for a successful vaginal birth after cesarean section provided to them is a general one. The probability of having a successful vaginal birth is the cornerstone factor of the mothers' decision. Therefore, providing a case-specific likelihood that respects the characteristics of each pregnancy will refine counseling, lower the decision conflict, and improve the success rate of vaginal birth trials eventually improving maternal and fetal outcomes. This paper aims to develop a clinical decision support system to evaluate the individualized likelihood mode of delivery for pregnant women with a previous lower segment cesarean section based on their unique characteristics. The study included six hundred fifty-nine pregnant women, where three hundred twenty-seven records had missing values. Various pre-processing steps, including missing data imputation and feature selection, were applied to the original dataset before model development to improve the data quality. Missing values were handled first, then a feature selection process using a genetic algorithm was applied to select the relevant features and to exclude features that may have been affected negatively by missing data imputation. After that, four machine learning classifiers, namely Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression, were used to build the prediction model. The results showed that imputing missing values followed by feature selection was more efficient than deleting them since the Area Under the Curve (AUC) has increased from 0.655 to 0.812 using the KNN classifier.

Keywords: Data imputation; Missing data; Vaginal birth after cesarean (VBAC).