Husayn01/Team-Q

Building an AI-powered health intelligence system designed to improve healthcare delivery across Africa. It integrates predictive analytics, geospatial mapping, and conversational AI to monitor health trends, support diagnosis, and optimize resource allocation. # AfriHealth AI - Intelligent Primary Healthcare Management Platform ## Executive Summary AfriHealth AI is a comprehensive, production-ready healthcare intelligence platform designed to strengthen Primary Healthcare (PHC) delivery across Africa. Developed to address the critical challenges facing PHC centers - where less than 20% are fully functional in Nigeria - this solution leverages artificial intelligence, predictive analytics, and real-time data processing to transform healthcare delivery at the grassroots level. ## Problem Statement Primary Healthcare Centers across Africa face systemic challenges that impede progress toward Universal Health Coverage: - **Nigeria**: Less than 20% of PHCs are fully functional, with poor staffing and limited diagnostic capacity - **Kenya**: Inadequate infrastructure and shortage of skilled health workers - **Uganda**: Insufficient funding and gaps in medical supplies - **South Africa**: Long waiting times and disparities in service quality These bottlenecks result in delayed diagnoses, inefficient patient allocation, poor data utilization, and inequitable access to healthcare services. ## Solution Overview AfriHealth AI delivers an integrated, AI-powered platform that provides: - **Real-time Analytics Dashboard** - Comprehensive monitoring of 1000+ healthcare facilities - **Predictive ML Models** - Three specialized models for proactive healthcare management - **Intelligent Resource Allocation** - Data-driven recommendations for staff and inventory - **AI Health Assistant** - Natural language interface for healthcare insights - **Geographic Visualization** - Interactive maps for spatial health analysis ## Technical Architecture ### Technology Stack - **Frontend**: React 18, Vite, Tailwind CSS, Recharts, Leaflet Maps - **Backend**: FastAPI, Python 3.9+, Pandas, NumPy, Scikit-learn - **Machine Learning**: RandomForest, XGBoost, Time Series Forecasting - **Data Processing**: In-memory caching with TTL, Real-time aggregatio …