Exploring Machine Learning Algorithms to Find the Best Features for Predicting Modes of Childbirth

Abstract

The mode of delivery is a crucial determinant for ensuring the safety of both mother and child. The current practice for predicting the mode of delivery is generally the opinion of the physician in charge, but choosing the wrong method of delivery can cause different short-term and long-term health issues for both mother and baby. The purpose of this study was twofold: first, to reveal the possible features for determining the mode of childbirth, and second, to explore machine learning algorithms by considering the best possible features for predicting the mode of childbirth (vaginal birth, cesarean birth, emergency cesarean, vacuum extraction, or forceps delivery). An empirical study was conducted, which included a literature review, interviews, and a structured survey to explore the relevant features for predicting the mode of childbirth, while five different machine learning algorithms were explored to identify the most significant algorithm for prediction based on 6157 birth records and a minimum set of features. The research revealed 32 features that were suitable for predicting modes of childbirth and categorized the features into different groups based on their importance. Various models were developed, with stacking classification (SC) producing the highest f1 score (97.9%) and random forest (RF) performing almost as well (f1-score = 97.3%), followed by k-nearest neighbors (KNN; f1-score = 95.8%), decision tree (DT; f1-score = 93.2%), and support vector machine (SVM; f1-score = 88.6%) techniques, considering all (n = 32) features.

Keywords

• Machine learning
• prediction
• vaginal childbirth
• cesarean childbirth
• data mining
• childbirth