From Prediction to Action: Counterfactual Explanations and Ensemble Learning for Explainable Maternal Health Risk Modelling
- Title
- From Prediction to Action: Counterfactual Explanations and Ensemble Learning for Explainable Maternal Health Risk Modelling
- Creator
- Monson, Merlin; Sabarmathi, G.
- Description
- 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.
- Source
- 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Counterfactual explanations; DiCE; Ensemble model; Explainable AI; Maternal health; Stacking model
- Coverage
- Monson M., Christ (University), School of Business and Management, Karnataka, Bengaluru, India; Sabarmathi G., Christ (University), School of Business and Management, Karnataka, Bengaluru, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833159610-1;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Monson, Merlin; Sabarmathi, G., “From Prediction to Action: Counterfactual Explanations and Ensemble Learning for Explainable Maternal Health Risk Modelling,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25915.