Prediction of Cesarean Childbirth using Ensemble Machine Learning Methods

Abstract

Cesarean section around the world is increasing at an alarming rate. Cesarean section, on one hand, may introduce different short-term and long-term complications for mother; on another hand it may be a life-saving procedure for both mother and child, depending on childbirth complications. The purpose of this research is to predict whether or not the cesarean section is necessary with the help of data mining and consequently, increasing the safety of the mother and newborn during and after childbirth by avoiding unnecessary cesarean section. To attain the objective three different ensemble prediction models based on- XGBoost, AdaBoost and Catboost were developed. As an outcome XGBoost showed the highest accuracy-88.91% while AdaBoost showed 88.69% accuracy and Catboost showed 87.66% accuracy. This research also revealed that amniotic liquid, medical indication, fetal intrapartum ph, number of previous cesareans, pre-induction are the most influential features for predicting the target outcome accurately.