- Academic Editor
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Background: Cardiovascular diseases (CVD) remain the predominant global
cause of mortality, with both low and high temperatures increasing CVD-related
mortalities. Climate change impacts human health directly through temperature
fluctuations and indirectly via factors like disease vectors. Elevated and
reduced temperatures have been linked to increases in CVD-related
hospitalizations and mortality, with various studies worldwide confirming the
significant health implications of temperature variations and air pollution on
cardiovascular outcomes. Methods: A database of daily Emergency Room
admissions at the Giovanni XIII Polyclinic in Bari (Southern Italy) was
developed, spanning from 2013 to 2019, including weather and air quality data. A
Random Forest (RF) supervised machine learning model was used to simulate the
trend of hospital admissions for CVD. The Seasonal and Trend decomposition using
Loess (STL) decomposition model separated the trend component, while
cross-validation techniques were employed to prevent overfitting. Model
performance was assessed using specific metrics and error analysis. Additionally,
the SHapley Additive exPlanations (SHAP) method, a feature importance technique
within the eXplainable Artificial Intelligence (XAI) framework, was used to
identify the feature importance. Results: An R