Bridging the Trust Gap: Clinician-Validated Hybrid Explainable AI for Maternal Health Risk Assessment in Bangladesh

TL;DR

This paper introduces a hybrid explainable AI framework for maternal health risk assessment in Bangladesh, combining fuzzy logic and SHAP explanations, validated by clinicians to improve trust and practical utility.

Contribution

It presents a novel hybrid XAI approach integrating ante-hoc fuzzy logic with post-hoc SHAP explanations, validated through clinician feedback in a resource-constrained setting.

Findings

Achieved 88.67% accuracy with fuzzy-XGBoost on maternal health data.

Majority of clinicians preferred hybrid explanations for trust.

Identified healthcare access as the key predictor, validating clinical knowledge.

Abstract

While machine learning shows promise for maternal health risk prediction, clinical adoption in resource-constrained settings faces a critical barrier: lack of explainability and trust. This study presents a hybrid explainable AI (XAI) framework combining ante-hoc fuzzy logic with post-hoc SHAP explanations, validated through systematic clinician feedback. We developed a fuzzy-XGBoost model on 1,014 maternal health records, achieving 88.67% accuracy (ROC-AUC: 0.9703). A validation study with 14 healthcare professionals in Bangladesh revealed strong preference for hybrid explanations (71.4% across three clinical cases) with 54.8% expressing trust for clinical use. SHAP analysis identified healthcare access as the primary predictor, with the engineered fuzzy risk score ranking third, validating clinical knowledge integration (r=0.298). Clinicians valued integrated clinical parameters but identified critical gaps: obstetric history, gestational age, and connectivity barriers. 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.