Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh

Abstract

Maternal mortality and childbirth complications are major delivery issues in most developing countries, especially in rural areas. The proper identification of risks associated with the delivery of an expecting woman at an earlier stage can substantially reduce the mortality rate. A few studies have been conducted on using Machine Learning (ML) techniques for predicting birth mode i.e. caesarean section or normal delivery. The most commonly used methods are Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). In this study we have implemented Bagging Ensemble Classifiers based on these traditional machine learning algorithms, which is a novel approach in the area of birth mode prediction. This paper examines the performance of four ML algorithms, with bagging ensemble classifiers (DT-Bagging, KNN-Bagging, NB-Bagging, SVM-Bagging). The result shows that bagging ensemble models outperformed the traditional models in this domain. Besides, we have identified the association between important factors and caesarean section. This study may later be used to create a decision support system by extracting knowledge from the hidden patterns in data to reduce the rate of caesarean delivery in Bangladesh.