标题：Using artificial intelligence systems to investigate relation between air pollution and acute respiratory symptoms registered at the Emergency Medical Center of Mashhad in 2017
摘要：Background and Aim: One of the most significant environmental problems is air pollution and has a remarkable impact on the incidence of cardiovascular disease and mortality. It is essential to correctly comprehend air pollution affects and ways of spreading, and predict the number of patients with acute respiratory problems to eliminate and reduce air pollutants and mortality due to these diseases. The aim of this study is to represent the relation between different factors of air pollution and its effect on the number of cardiovascular patients in Mashhad. Materials and Methods: This study applied a neural network to model and analyze the relationship between CO, NO2, SO2, PM2.5 and PM10 with number of patients with acute respiratory problems. The inputs are average temperature, humidity, direction and wind speed and model output is the number of people referred per day by gender and age. The data set used includes meteorological data from the Iran Meteorological Organization, air pollution data from the Mashhad Meteorological Organization and data the number of daily referrals of heart patients to the emergency department of Mashhad. Results: According to this study, the main air pollutants in Mashhad are PM2.5, PM10 and the other pollutants are NO2, CO and SO2, respectively. Conclusion: Neural networks can be applied in modeling to discover the relationship between environmental parameters in cardiovascular patients because they have a high ability to model nonlinear phenomena. These models show that the more airborne particles increase, the more rate of cardiovascular diseases increases in Mashhad.
其他摘要：Background and Aim: One of the most significant environmental problems is air pollution and has a remarkable impact on the incidence of cardiovascular disease and mortality. It is essential to correctly comprehend air pollution affects and ways of spreading, and predict the number of patients with acute respiratory problems to eliminate and reduce air pollutants and mortality due to these diseases. The aim of this study is to represent the relation between different factors of air pollution and its effect on the number of cardiovascular patients in Mashhad. Materials and Methods: This study applied a neural network to model and analyze the relationship between CO, NO2, SO2, PM2.5 and PM10 with number of patients with acute respiratory problems. The inputs are average temperature, humidity, direction and wind speed and model output is the number of people referred per day by gender and age. The data set used includes meteorological data from the Iran Meteorological Organization, air pollution data from the Mashhad Meteorological Organization and data the number of daily referrals of heart patients to the emergency department of Mashhad. Results: According to this study, the main air pollutants in Mashhad are PM2.5, PM10 and the other pollutants are NO2, CO and SO2, respectively. Conclusion: Neural networks can be applied in modeling to discover the relationship between environmental parameters in cardiovascular patients because they have a high ability to model nonlinear phenomena. These models show that the more airborne particles increase, the more rate of cardiovascular diseases increases in Mashhad
