Premature Birth Prediction Using Machine Learning Algorithms – A Comparative Analysis

Premature birth (PB) presents a significant global health challenge, often leading to enduring developmental issues and neonatal mortality. Approximately 4-16% of new-born worldwide is born prematurely. Leveraging machine learning, a predictive model has been developed to anticipate preterm birth risk. This model evaluates maternal characteristics and other pertinent factors to provide timely alerts to healthcare providers. Through the analysis of extensive datasets, the system offers valuable insights for forecasting and classifying preterm birth risks. First and foremost, this study aims to enhance prenatal care and outcomes for high-risk pregnancies by harnessing artificial intelligence to refine the accuracy and promptness of preterm birth prediction. Employing machine learning algorithms, our study achieves a remarkable 99% accuracy rate throughout all the techniques.