Premature Birth Prediction Using Machine Learning Techniques

Premature babies are the most vulnerable to neonatal mortality, and their birth process is emotionally and physically painful for the mother and the family. The well-being of a premature baby can also be financially burdensome. This paper looks into the maternal factors associated with prematurity. The surveys took place in public maternity hospitals at the Western Brazilian Amazon. This research aims to predict whether a baby will be born prematurely using numerous distinct models. In order to serve this purpose, various machine learning classification algorithms (Decision tree, Naive Bayes, Random Forest, Extreme Gradient Boosting, and K-NearestNeighbors) were applied to the preprocessed data. The paper proposes models capable of predicting premature birth with an accuracy of about 80%. This research aids in developing a usable model that can detect premature births at an early stage, which will allow early treatment to prevent premature birth, substantially reducing child mortality and reducing the economic stress on families bearing a premature child.