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Malaria AI — Early Detection & Decision Support Framework
Kenya · Synthetic Surveillance Dataset · n = 100,000 · 15 Counties · 4 Endemic Zones
📍 Malaria Prevalence by Endemic Zone
🔬 Diagnostic Test Performance
👥 Age & Sex Distribution
🏥 Key Risk Factor Prevalence (Positive Cases)
🔧 Filter Data
🗺️ County-Level Risk Profile
📊 Zone Risk Ranking
🌡️ Environmental Drivers
🌧️ Rainfall vs Malaria Prevalence
🏔️ Altitude vs Malaria Risk Index
⚙️ Training Configuration
👤 Patient Demographics
🌡️ Clinical Symptoms
🌿 Environmental & Exposure
🏠 Socioeconomic & Prevention
🤖 AI Prediction Engine
📊 Probability Gauge
💊 Clinical Decision Support
🧠 Explainable AI: Local Feature Contributions
This plot shows how each patient feature pushed the prediction away from the average risk. Green bars increase the risk of malaria. Blue bars decrease it.
⚙️ Alert Configuration
🚨 Active Alerts by County
📋 High-Risk Case List
📋 AI-Generated Clinical Intelligence Report
📎 Dataset Context
The AI has access to live summary statistics from the loaded dataset. Toggle context layers to include:
💡 Suggested Questions
🗑️ Session