Machine learning and XAI approaches highlight the strong connection between $$O_3$$ O 3 and $$NO_2$$ N O 2 pollutants and Alzheimer’s disease

Abstract

Abstract Alzheimer’s disease (AD) is the most common type of dementia with millions of affected patients worldwide. Currently, there is still no cure and AD is often diagnosed long time after onset because there is no clear diagnosis. Thus, it is essential to study the physiology and pathogenesis of AD, investigating the risk factors that could be strongly connected to the disease onset. Despite AD, like other complex diseases, is the result of the combination of several factors, there is emerging agreement that environmental pollution should play a pivotal role in the causes of disease. In this work, we implemented an Artificial Intelligence model to predict AD mortality, expressed as Standardized Mortality Ratio, at Italian provincial level over 5 years. We employed a set of publicly available variables concerning pollution, health, society and economy to feed a Random Forest algorithm. Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly $$O_3$$ O 3 and $$NO_2$$ N O 2 ) contribute the most to AD mortality prediction. These results could help to shed light on the etiology of Alzheimer’s disease and to confirm the urgent need to further investigate the relationship between the environment and the disease.

Keywords

• Alzheimer
• Machine learning
• Explainable artificial intelligence
• Pollution
• Standardized mortality ratio (SMR)
• One health