An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers

Abstract

ABSTRACTThe desire for safer delivery mode that preserves the lives of both mother and child with minimal or no complications before, during and after childbirth is the wish for every expectant mother and their families. However, the choice for any particular birth delivery mode is determined primarily by a number of factors that feed into the ultimate decision of choice. Some of these include maternal birth history, maternal and child health conditions prevailing before and during labor onset. Predictive modeling has been used extensively to determine important contributory factors influencing delivery choice. However, missing among a myriad of features used in various research studies for this determination is maternal history of spontaneous, threatened and inevitable abortion(s) and how its inclusion can impact delivery outcomes. This research work therefore takes measurable maternal features that include real time information on administered partographs to predict delivery type. This is achieved by adopting effective feature selection technique to estimate variable relationship with the target variable. Three supervised learning techniques are used and evaluated for performance. Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score for the same learning techniques obtained were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. Correlation statistic for variable independence among input variables showed that delivery type as an output is associated with gestational age and the progress of maternal cervix dilatation during labor onset.