Approved Research

Whole-climate risk factors analysis of acute myocardial infarction (AMIs) based on machine learning modeling.

Lay summary

Epidemiological and clinical evidence has heightened concerns about the associations between climate factors and cardiovascular disease. The climate factors include daily temperature, temperature variability, humidity, wind speed, noise, etc. We assume that cardiovascular disease is correlated with previous factors from the perspective of considering climate changes. However, the specific impact of various climate factors on the occurrence of cardiovascular disease is unclear, and the effect of whole-climate risk factors is unclear, either.

We aim to establish a prediction model of whole-climate factors for the risk of cardiovascular disease by machine learning modeling based on UK biobank data. We assume that this system, by reminding people to adapt to climate change ahead of time, may reduce the risk of cardiovascular disease in the future.