Momahmoses/nigeria-maternal-mortality-prediction

||XGBoost and SHAP maternal mortality risk scoring for Nigerian PHCs with a DHIS2-compatible REST API and SMS alerts for community health workers. # Maternal Mortality Prediction & Intervention Engine XGBoost + SHAP maternal mortality risk scoring for Nigerian Primary Health Centres, DHIS2-compatible REST API with SMS alerts for community health workers to intervene before emergencies occur. --- ## Problem Statement Nigeria accounts for ~20% of global maternal deaths, most are preventable with timely intervention. Community health workers lack a systematic risk screening tool for antenatal visits. This engine scores every pregnant woman's risk at registration and triggers alerts for high-risk cases. --- ## Features | Feature | Description | |---------|-------------| | XGBoost Risk Scoring | Probability of adverse maternal outcome per patient | | SHAP Explainability | Per-patient risk factor breakdown for health workers | | DHIS2 Integration | REST API compatible with Nigeria's national health data system | | SMS Alerts | Automatic Twilio SMS to CHWs for high-risk cases | | Geospatial Risk Map | State-level maternal mortality risk dashboard | --- ## Tech Stack | Layer | Technology | |-------|-----------| | Machine Learning | XGBoost, SHAP | | API | FastAPI, Uvicorn | | Alerts | Twilio SMS | | Geospatial | GeoPandas, Folium | | Data | pandas, NumPy | --- ## Quick Start ```bash git clone github.com cd nigeria-maternal-mortality-prediction pip install -r requirements.txt uvicorn app:app --host 0.0.0.0 --port 8000 ``` --- ## Data Sources - NDHS (Nigeria Demographic and Health Survey) - DHIS2 Nigeria PHC antenatal records - WHO maternal health indicators - GRID3 health facility geolocation data --- ## Author **Momah Moses**, Geospatial AI Engineer & Data Scientist GitHub · Portfolio