## 4 Discussion  
This study analyzed the association between air pollution and mortality from endocrine, nutritional, and metabolic diseases, including geolocalized data on CO, NO, NO\({}_{2}\), O\({}_{3}\), PM10, PM2.5, and SO\({}_{2}\), and considering demographic and socio-economic variations such as age and gender. The standardized mortality ratio (SMR) was calculated as the ratio between observed deaths in a provincial area and expected ones. Our analysis revealed that the majority of the input variables examined in this study exhibited weak correlations with the target variable, the standardized mortality ratio (SMR). However, the Gradient Boosting model demonstrated a strong predictive accuracy, particularly for provinces with SMR values above the mean. The model achieved a coefficient of determination (\(r^{2}\)) of 0.55 and a mean absolute error (MAE) of 0.17, indicating its effectiveness in capturing the relationship between environmental pollution and mortality from endocrine, nutritional, and metabolic diseases. These results highlight the significant role of environmental factors in influencing health outcomes and suggest that such findings should be integrated with other risk factors in studies exploring individual-level risk assessments, including personalized health contexts. Additionally, the application of Explainable AI (XAI) techniques provided valuable insights into the most influential global and local variables driving the model's predictions. The SHAP algorithm was used to identify the most influential variables in the model, highlighting the link between environmental factors, socio-economic factors, and population health. Quality of life emerged as the most important factor: in areas where it was higher, lower mortality rates were observed [32; 33]. This result emphasizes how a healthy lifestyle, access to services, and a favorable environment can positively impact health. At the same time, life expectancy is closely linked to mortality rates: in areas where people live shorter lives, the impact of endocrine diseases seems to be particularly relevant. High values of the variable photovoltaic panel significantly influenced the GB regressor's predictions of elevated mortality rates for endocrine, nutritional, and metabolic diseases. However, this pattern can be attributed to the geographic distribution of photovoltaic  
Figure 5: SHAP summary plot that visualizes the relative importance of features in the Gradient Boosting (GB) model for predicting mortality due to endocrine, nutritional, and metabolic diseases. The y-axis lists the features ranked by their average SHAP value, with the most influential features at the top. The x-axis represents the SHAP values, indicating the impact of each feature on the model’s predictions. panels in Italy. Provinces with a density of solar panels per square kilometer exceeding the national average are predominantly located in southern Italy, the same region with a higher SMR for these diseases. Although no scientific studies have examined a direct correlation between the percentage of students who did not complete primary education (variable instruction) and deaths from endocrine, nutritional, and metabolic diseases, it is well established that lower education levels are associated with less healthy behaviors and reduced access to health information factors that can negatively impact overall health [34, 35]. Education plays a crucial role in the prevention and management of chronic diseases, including endocrine, nutritional, and metabolic disorders. Therefore, while there is no direct evidence of a specific correlation, it is plausible that a higher dropout rate may indirectly contribute to an increase in mortality from these conditions. Among the pollutants analyzed, benzene stands out for its correlation with higher mortality rates. This is concerning, considering that benzene is often produced by activities such as vehicular traffic and combustion [36].