Prediction Of Maternal Health Risk Factors Using Machine Learning Algorithms

Ensuring the well-being of both mother and child during pregnancy requires diligent monitoring of maternal health. This study explores the application of machine learning (ML) models to predict maternal health risks, using a dataset of 1014 instances with six health metrics. The risk categories- low, medium and high- were predicted using several ML techniques, including Ensemble (Bagged Trees), k-NN, and Neural Networks. Each model was evaluated based on its accuracy, confusion matrices, and other key metrics. The Ensemble Bagged Trees method achieved the highest accuracy of 84.12%, outperforming other models. The findings in this paper demonstrate the potential of these ML techniques to provide timely and accurate risk assessments for maternal health risk in pregnant women.