MamaCare AI: A GeoAI Framework for Maternal Healthcare Accessibility Analysis in Underserved Communities

Maternal mortality remains a critical public health challenge in Northern Nigeria, where geographic barriers significantly delay access to essential healthcare services. In Kebbi State, approximately 62% of women of reproductive age reside more than 5 km from the nearest health facility, representing a severe and measurable accessibility gap. This paper presents MamaCare AI, a GeoAI framework integrating geospatial intelligence and machine learning to support maternal healthcare accessibility analysis and evidence-based intervention planning in underserved communities. Using GRID3 facility data (1,207 facilities), WorldPop population estimates, and Nigeria DHS-derived indicators, we conduct a spatial accessibility analysis across 1,510 grid points in Kebbi State. DBSCAN spatial clustering (silhouette score: 0.83) identifies priority underserved population zones, and a composite risk scoring model across all 21 Local Government Areas identifies Argungu, Augie, and Dandi as critical intervention priorities. Based on these findings, we propose a phased AI-assisted mobile clinic deployment strategy targeting 310,000 women across three priority LGAs. This work demonstrates how GeoAI methods can translate spatial health data into actionable intervention frameworks for low-resource maternal healthcare environments.