AI models link Sri Lanka's air quality to respiratory disease risk

A new study published on arXiv details a district-level analysis of respiratory disease drivers in Sri Lanka, integrating environmental data with health admission rates. Researchers developed two XGBoost models to predict respiratory rates and PM2.5 concentrations, achieving high accuracy. The analysis, using SHAP values, identified air quality as the primary driver of respiratory variance, followed by forest degradation and fire activity, leading to the creation of a Forest-Air-Health (FAH) Risk Index. AI

IMPACT Provides a data-driven framework for public health and environmental policy in Sri Lanka, demonstrating AI's utility in environmental health research.

RANK_REASON The cluster contains a research paper published on arXiv detailing a novel analysis using machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Colombo
• Forest-Air-Health (FAH) Risk Index
• Gampaha
• Kalutara
• nitrogen dioxide
• PM2.5
• Shapley Additive Explanations
• Sri Lanka
• sulfur dioxide
• XGBoost

• paper
• other

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