A Machine Learning Approach to Predicting Household Smoke Exposure Risk in Somalia: An Analysis With SHAP Explanations

Abstract

This is the first national study to apply machine learning to predict SER in Somalia, revealing urban and wealth-linked vulnerabilities that challenge conventional assumptions about poverty. These findings highlight the need for targeted clean cooking interventions in urban and peri-urban communities, alongside data innovations for real-time monitoring. ML-informed risk stratification may support more effective and equitable health policies in fragile states.