An ensemble model for maternal health risk classification in Delta State, Nigeria

Maternal mortality remains a critical challenge in Sub-Saharan Africa, with Nigeria ranking among the countries with the highest rates. The loss of women in their reproductive years destabilizes families causing emotional trauma, places additional strain on healthcare systems, and has profound economic and national developmental consequences. As a result, one of the United Nations Sustainable Development goals (SDGs) is targetted at reducing maternal mortality and morbidity at all cost. This study explores the application of Artificial Intelligence (AI) in healthcare through the development of a predictive ensemble model to classify maternal health risks as identifying high risk pregnancies can inform timely clinical decision making that mitigates maternal mortality. Maternal health dataset was sourced from three (3) health centers in Delta State, Nigeria.. Nine supervised machine learning classifiers were utilized, including Linear Support Vector Machine, Gaussian Naïve Bayes, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting, Light Gradient Boosting Machine, and Categorical Boosting. To enhance predictive performance, the classifiers were combined in an ensemble model. Results showed that the Gradient Boosting Decision Tree achieved the highest accuracy at 90% before upsampling and Random Forest achieved an accuracy of 97% at upsampling. The lowest-performing classifier was Linear Support Vector Machine before and after upsampling. The ensemble model surpassed all individual classifiers, achieving 98% accuracy and precision and over 1% increase in accuracy after upsampling. This study highlights the potential of AI-driven predictive models to optimize healthcare resources and improve maternal health outcomes in Delta State, Nigeria.