Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Monson, Merlin; Sabarmathi, G. Title A name given to the resource From Prediction to Action: Counterfactual Explanations and Ensemble Learning for Explainable Maternal Health Risk Modelling Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAMS65118.2025.11234042" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAMS65118.2025.11234042</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030065420?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030065420?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Monson M., Christ (University), School of Business and Management, Karnataka, Bengaluru, India; Sabarmathi G., Christ (University), School of Business and Management, Karnataka, Bengaluru, India Description An account of the resource Maternal health is critical to women's well-being, particularly during pregnancy, delivery, and postpartum. Early prediction and prevention of health risks are essential for reducing complications and improving outcomes. This research introduces a stacking ensemble model for maternal health risk prediction, combining the strengths of Random Forest, XGBoost, and Gradient Boosting with XGBoost as the meta-model. The ensemble approach enhances accuracy and reliability, achieving a classification accuracy of 91.13%, with precision, recall, and f1-scores exceeding 85% across all risk categories.Beyond accurate prediction, this study emphasizes model interpretability through Diverse Counterfactual Explanations (DiCE), an Explainable AI (XAI) method that provides actionable insights for risk reduction. Counterfactual analysis identifies the minimal changes needed in the patient features to shift a high- or medium-risk classification to low-risk, offering clinically relevant recommendations. These counterfactuals are generated to ensure feasibility, preserving physiological plausibility and practical applicability for healthcare professionals. This work bridges the gap between black-box machine learning models and actionable decision-making by integrating predictive power with explainability, supporting more transparent and patient-centric maternal health interventions. 2025 IEEE. Subject The topic of the resource Counterfactual explanations; DiCE; Ensemble model; Explainable AI; Maternal health; Stacking model Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159610-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online