Analyzing the Interactions between Environmental Parameters and Cardiovascular Diseases Using Random Forest and SHAP Algorithms

• Published in: Reviews in cardiovascular medicine Vol. 24; no. 11; p. 330
• Main Authors: Castronuovo, Gianfranco, Favia, Gianfranco, Telesca, Vito, Vammacigno, Andrea
• Format: Journal Article
• Language: English
• Published: Singapore IMR Press 01.11.2023
• Subjects: cardiovascular diseases; climate; feature importance; hospital admissions; Original Research; random forest; time series decomposition; xai - explainable artificial intelligence techniques; XAI - eXplainable Artificial Intelligence techniques; hospital admissions; feature importance; cardiovascular diseases; climate; random forest; time series decomposition
• ISSN: 1530-6550; 2153-8174; 2153-8174; 1530-6550
• DOI: 10.31083/j.rcm2411330

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 R2 of 0.97 and a Mean Absolute Error of 0.36 admissions were achieved by the model. Atmospheric pressure, minimum temperature, and carbon monoxide were found to collectively contribute about 74% to the model’s predictive power, with atmospheric pressure being the dominant factor at 37%. Conclusions: This research underscores the significant influence of weather-climate variables on cardiovascular diseases. The identified key climate factors provide a practical framework for policymakers and healthcare professionals to mitigate the adverse effects of climate change on CVD and devise preventive strategies.