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Climate change, including temperature fluctuations and air pollution, significantly impacts cardiovascular disease (CVD) mortality. This study used machine learning to identify key weather factors like atmospheric pressure influencing CVD hospital admissions.

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Area of Science:

  • Environmental Health
  • Cardiology
  • Data Science

Background:

  • Cardiovascular diseases (CVD) are a leading global cause of death.
  • Both extreme heat and cold increase CVD-related mortality and hospitalizations.
  • Climate change exacerbates CVD risks through temperature variations and air pollution.

Purpose of the Study:

  • To investigate the relationship between weather-climate variables and cardiovascular disease hospital admissions.
  • To develop a predictive model for CVD admissions using machine learning.
  • To identify key environmental factors influencing cardiovascular health.

Main Methods:

  • Utilized daily Emergency Room admissions data (2013-2019) with weather and air quality data.
  • Employed a Random Forest (RF) model for simulating CVD admission trends.
  • Applied Seasonal and Trend decomposition using Loess (STL) and SHapley Additive exPlanations (SHAP) for analysis.

Main Results:

  • Achieved a model performance of R-squared 0.97 and Mean Absolute Error of 0.36 admissions.
  • Atmospheric pressure, minimum temperature, and carbon monoxide were key predictors, accounting for 74% of predictive power.
  • Atmospheric pressure was the most influential factor, contributing 37%.

Conclusions:

  • Weather-climate variables significantly impact cardiovascular diseases.
  • Identified key climate factors offer a framework for public health strategies.
  • Findings aid policymakers and healthcare professionals in mitigating climate change effects on CVD.