Identification of Maternal Health Risk From Optimal Features Using Explainable Machine Learning
Mohammad Mamun, Safiul Haque Chowdhury, Md. Omar Faruq, Mir Mohammad Azad, Bishakha Rani Biswas, Mohammed Ibrahim Hussain, Muhammad Minoar Hossain · Engineering Reports · 2025
ABSTRACT Maternal health is a significant global crisis that affects women every day, resulting in severe complications related to pregnancy or childbirth. As a response to this urgent need for effective risk management, we have developed an automated system for maternal health risk (MHR) prediction that utilizes machine learning (ML) and explainable artificial intelligence (XAI). Our research aims to enhance the accuracy and efficiency of risk assessment by employing rigorous preprocessing techniques on a dataset of 1014 samples. To achieve this goal, we employ 10 ensemble ML models and use diverse feature optimization methods such as principal component analysis (PCA), linear discriminant analysis (LDA), and recursive feature elimination (RFE). To clearly understand the decision‐making processes of the selected ensemble model, we employ XAI as Shapley additive explanations (SHAP) plots, local interpretable model‐agnostic explanation (LIME) plots, and individual conditional expectation (ICE) plots. Our evaluation includes various ML performance metrics and a variety of statistical measures, which demonstrate that the Bagging, in conjunction with PCA, emerges as the optimal model, achieving an impressive accuracy of 99.51%. This research uses 10‐fold cross‐validation throughout the whole analysis. By emphasizing proactive risk detection and personalized interventions, we aim to improve understanding of MHR and help women worldwide make informed decisions.