Predicting Childbirth Modes: A Comparative Analysis of Machine Learning Algorithm

The health of the mother and foetus, the course of labor, and the existence of any difficulties are some of the variables that might affect the complicated process of childbirth. A crucial job that can assist physicians in making better decisions regarding patient care is predicting whether a patient will give birth vaginally or by cesarean surgery. In order to evaluate and manage the risks to both the mother and the fetus, it is essential for maternal healthcare professionals to classify and anticipate childbirth. The development and comparison of several machine learning models for the categorization and prediction of delivery is the main goal of this work. The first step in the procedure is loading the information, which includes attributes like maternal education, height, weight, BMI, country of origin, age, and couple status. A comprehensive literature study is the first step in determining the importance of these characteristics and how they affect the outcomes of delivery. The dataset is refined using feature selection approaches, and then it is converted from categorical to numerical so that machine learning algorithms may use it. The training and testing datasets are then loaded for model construction and assessment. The usefulness of a number of classification techniques, such as decision trees, support vector machines, K-nearest neighbors, random forests, and stacking classifiers, in predicting childbirth is tested and compared. This work intends to improve maternal healthcare decision-making and outcomes by shedding light on the best machine learning technique for classifying and predicting delivery through testing and analysis.