Boosting Maternal Health: Explainable AI Bridges Trust Gap in Bangladesh

research #xai 🔬 Research|Analyzed: Jan 15, 2026 07:04•

Published: Jan 15, 2026 05:00

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1 min read

Analysis

This research showcases a practical application of XAI, emphasizing the importance of clinician feedback in validating model interpretability and building trust, which is crucial for real-world deployment. The integration of fuzzy logic and SHAP explanations offers a compelling approach to balance model accuracy and user comprehension, addressing the challenges of AI adoption in healthcare.

Key Takeaways

•Hybrid XAI framework (fuzzy-XGBoost) achieved 88.67% accuracy in maternal health risk assessment.
•Clinician feedback highlighted the value of hybrid explanations, with over 70% preferring them.
•Healthcare access was identified as the primary predictor by SHAP analysis.

Reference / Citation

View Original

"This work demonstrates that combining interpretable fuzzy rules with feature importance explanations enhances both utility and trust, providing practical insights for XAI deployment in maternal healthcare."

ArXiv AIJan 15, 2026 05:00

* Cited for critical analysis under Article 32.

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