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Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence

BACKGROUND: The prediction of the number of acute coronary syndromes (ACSs) 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. METHODS: Between 2008 and 2018, a total...

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Autores principales: Wlodarczyk, Aleksandra, Molek, Patrycja, Bochenek, Bogdan, Wypych, Agnieszka, Nessler, Jadwiga, Zalewski, Jaroslaw
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024050/
https://www.ncbi.nlm.nih.gov/pubmed/35463797
http://dx.doi.org/10.3389/fcvm.2022.830823
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author Wlodarczyk, Aleksandra
Molek, Patrycja
Bochenek, Bogdan
Wypych, Agnieszka
Nessler, Jadwiga
Zalewski, Jaroslaw
author_facet Wlodarczyk, Aleksandra
Molek, Patrycja
Bochenek, Bogdan
Wypych, Agnieszka
Nessler, Jadwiga
Zalewski, Jaroslaw
author_sort Wlodarczyk, Aleksandra
collection PubMed
description BACKGROUND: The prediction of the number of acute coronary syndromes (ACSs) 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. METHODS: Between 2008 and 2018, a total of 105,934 patients with ACS 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 6 days before ACS. RESULTS: 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 the seasons and stations. The number of ACS was higher in warm front days vs. days without any front [40 (29–50) vs. 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% CI of 0.80–0.84 (P < 0.001). The greatest importance for machine learning (range 0–1.0) among the parameters reached Td daily range with 1.00, pressure daily range with 0.875, pressure maximum daily range with 0.864, and RH maximum daily range with 0.853, whereas among the clinical parameters reached hypertension daily range with 1.00 and diabetes mellitus daily range with 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 from 0.72 to 0.76 (95% CI 0.66–0.81), for autumn from 0.72 to 0.83 (95% CI 0.67–0.87), and for winter from 0.76 to 0.79 (95% CI 0.71–0.83) (P < 0.001 for each). CONCLUSION: The weather parameters have proven useful in predicting the prevalence of ACS in a temperate climate zone for all the seasons, if analyzed with an artificial intelligence system. Simultaneously, the analysis of individual weather parameters or frontal scenarios has provided only weak univariate relationships. These findings will require validation in other climatic zones.
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spelling pubmed-90240502022-04-23 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 Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: The prediction of the number of acute coronary syndromes (ACSs) 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. METHODS: Between 2008 and 2018, a total of 105,934 patients with ACS 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 6 days before ACS. RESULTS: 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 the seasons and stations. The number of ACS was higher in warm front days vs. days without any front [40 (29–50) vs. 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% CI of 0.80–0.84 (P < 0.001). The greatest importance for machine learning (range 0–1.0) among the parameters reached Td daily range with 1.00, pressure daily range with 0.875, pressure maximum daily range with 0.864, and RH maximum daily range with 0.853, whereas among the clinical parameters reached hypertension daily range with 1.00 and diabetes mellitus daily range with 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 from 0.72 to 0.76 (95% CI 0.66–0.81), for autumn from 0.72 to 0.83 (95% CI 0.67–0.87), and for winter from 0.76 to 0.79 (95% CI 0.71–0.83) (P < 0.001 for each). CONCLUSION: The weather parameters have proven useful in predicting the prevalence of ACS in a temperate climate zone for all the seasons, if analyzed with an artificial intelligence system. Simultaneously, the analysis of individual weather parameters or frontal scenarios has provided only weak univariate relationships. These findings will require validation in other climatic zones. Frontiers Media S.A. 2022-04-08 /pmc/articles/PMC9024050/ /pubmed/35463797 http://dx.doi.org/10.3389/fcvm.2022.830823 Text en Copyright © 2022 Wlodarczyk, Molek, Bochenek, Wypych, Nessler and Zalewski. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Wlodarczyk, Aleksandra
Molek, Patrycja
Bochenek, Bogdan
Wypych, Agnieszka
Nessler, Jadwiga
Zalewski, Jaroslaw
Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence
title Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence
title_full Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence
title_fullStr Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence
title_full_unstemmed Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence
title_short Machine Learning Analyzed Weather Conditions as an Effective Means in the Predicting of Acute Coronary Syndrome Prevalence
title_sort machine learning analyzed weather conditions as an effective means in the predicting of acute coronary syndrome prevalence
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024050/
https://www.ncbi.nlm.nih.gov/pubmed/35463797
http://dx.doi.org/10.3389/fcvm.2022.830823
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