Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence - Wlodarczyk, Aleksandra; Molek, Patrycja; Bochenek, Bogdan; Wypych, Agnieszka; Nessler, Jadwiga; Zalewski, Jaroslaw (Frontiers in Cardiovascular Medicine, 2022)
The prediction of the number of acute coronary syndromes (ACS) based on the weather conditions in the individual climate zones is not effective. We sought to investigate whether an artificial intelligence system might be useful in this prediction. Between 2008 and 2018, 105 934 ACS patients were hospitalized in Lesser Poland Province, one covered by two meteorological stations. The predicted daily number of ACS has been estimated with the Random Forest machine learning system based on air temperature (°C), air pressure (hPa), dew point temperature (Td, °C), relative humidity (RH, %), wind speed (m/s) and precipitation (mm) and their daily extremes and ranges derived from the day of ACS and from six days before ACS. Of 840 pairwise comparisons between individual weather parameters and the number of ACS, 128 (15.2%) were significant but weak with the correlation coefficients ranged from -0.16 to 0.16. None of weather parameters correlated with the number of ACS in all seasons and stations. The number of ACS was higher in warm front days versus days without any front (40 [29-50] versus 38 [27-48], respectively, P<0.05). The correlation between the predicted and observed daily number of ACS derived from machine learning was 0.82 with 95% confidence interval (95% CI) of 0.80-0.84 (P<0.001). The highest variable importance for machine learning (range 0-1.0) among whether parameters reached Td daily range with 1.00, pressure daily range with 0.875, pressure maximum with 0.864 and RH maximum with 0.853, whereas among the clinical parameters hypertension 1.00 and diabetes mellitus 0.28. For individual seasons and meteorological stations, the correlations between the predicted and observed number of ACS have ranged for spring from 0.73 to 0.77 (95% CI 0.68-0.82), for summer 0.72-0.76 (95% CI 0.66-0.81), for autumn 0.72-0.83 (95%CI 0.67-0.87) and for winter 0.76-0.79 (95% CI 0.71-0.83) (P<0.001 for each). The weather parameters have proven useful in predicting the prevalence of ACS in a temperate climate zone for all seasons if analyzed with an artificial intelligence system. Simultaneously, the analysis of individual weather parameters or frontal scenarios have provided only weak univariate relationships. These findings will require validation in other climatic zones.