Air pollution and mortality for cancer of the respiratory system in Italy
An explainable artificial intelligence approach
Bibliographic Data
Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors
Cancer · Chart · Environmental health · Machine learning · Random forest · Statistics · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Climate Change and Health Impacts · Computer Science · Mathematics · Medicine · Artificial Intelligence
Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016
From local explanations to global understanding with explainable AI for trees
Explainable Artificial Intelligence (XAI)
Random Forests
Environmental and Health Impacts of Air Pollution
Environmental justice and air pollution
| Citation velocity | historical |
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| Highly cited | No |