Background: Several studies suggest that environmental and climatic factors are linked to the risk of mortality due to cardiovascular and respiratory diseases; however, it is still unclear which are the most influential ones. This study sheds light on the potentiality of a data-driven statistical approach by providing a case study analysis. Methods: Daily admissions to the emergency room for cardiovascular and respiratory diseases are jointly analyzed with daily environmental and climatic parameter values (temperature, atmospheric pressure, relative humidity, carbon monoxide, ozone, particulate matter, and nitrogen dioxide). The Random Forest (RF) model and feature importance measure (FMI) techniques (permutation feature importance (PFI), Shapley Additive exPlanations (SHAP) feature importance, and the derivative-based importance measure (${\kappa }^{ALE}$)) are applied for discriminating the role of each environmental and climatic parameter. Data are pre-processed to remove trend and seasonal behavior using the Seasonal Trend Decomposition (STL) method and preliminary analyzed to avoid redundancy of information. Results: The RF performance is encouraging, being able to predict cardiovascular and respiratory disease admissions with a mean absolute relative error of 0.04 and 0.05 cases per day, respectively. Feature importance measures discriminate parameter behaviors providing importance rankings. Indeed, only three parameters (temperature, atmospheric pressure, and carbon monoxide) were responsible for most of the total prediction accuracy. Conclusions: Data-driven and statistical tools, like the feature importance measure, are promising for discriminating the role of environmental and climatic factors in predicting the risk related to cardiovascular and respiratory diseases. Our results reveal the potential of employing these tools in public health policy applications for the development of early warning systems that address health risks associated with climate change, and improving disease prevention strategies.

The increasing complexity in hospital waste management requires innovative solutions that integrate sustainability and regulatory compliance. This study proposes an AI-based decision tool to support the circular management of healthcare waste. The approach combines two key elements: (i) the systematic qualitative analysis of international, European, and national regulations, scientific literature, and best practices aimed at identifying strategic actions; (ii) the prioritization of these actions through machine learning, using a Random Forest classifier. We identified 55 actions, grouped into 13 thematic areas, and used them as input variables to assess their impact on regulatory compliance. The variable importance analysis allowed us to classify actions according to their strategic relevance, guiding the structure of the tool and its user interface. Validation, conducted on four simulated case studies, demonstrated the system’s ability to improve compliance monitoring, operational efficiency, and the implementation of circular economy and Zero-Waste strategies. The proposed model represents a scalable and evidence-based solution capable of supporting the ecological transition of healthcare facilities in line with EU directives and the Sustainable Development Goals.