Leveraging Machine Learning for Comprehensive Analysis of Maternal Health: Predicting Health Risks and Improving Antenatal Care

The health of the mother is crucial to the well-being of the baby throughout pregnancy. Early treatments and individualized care may be more effective when maternal health hazards are properly classified. In this study, we explore the feasibility of using fundamental and boosting machine learning algorithms to categorize threats to maternal health. On an extensive collection of maternal health indicator variables, we test the efficacy of many machine learning techniques, including logistic regression, decision trees, random forest, gradient boosting, and XGBoost. With an experimental accuracy in classification of 86.48%, our data show that XGBoost performs well. To further verify XGBoost’s efficacy, we use several assessment criteria, such as the lift curve and the ROC curve. These results provide important insight into how machine learning might be used to enhance prenatal care and lessen associated hazards to mothers.