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نوع مقاله : مقالات پژوهشى اصیل کمی و کیفی

نویسندگان

1 دانشگاه علوم پزشکی مشهد

2 معاون آموزشی وزارت بهداشت درمان و آموزش پزشکی، استاد اپیدمیولوژی و آمار زیستی دانشگاه علوم پزشکی کرمان

3 گروه مهندسی الکترونیک و مهندسی پزشکی- دانشگاه خیام-مشهد-ایران

چکیده

زمینه و هدف: آلودگی هوا، از مهم‌ترین عوامل موثر در بروز بیماری­های قلبی- عروقی و مرگ‌و‌میر ناشی از آن است. شناخت صحیح چگونگی تأثیر آلودگی، راه­های انتشار و پیش­بینی تعداد بیماران دارای مشکلات حاد تنفسی، پیاده­سازی راه­حل­های مناسب برای حذف و کاهش آلاینده­های هوا و کاهش مرگ‌و‌میر ناشی از بیماری­های مذکور ضروری می­باشد. مطالعه حاضر با هدف بیان رابطه عوامل مختلف آلودگی هوا و تأثیر آن بر تعداد بیماران قلبی- عروقی در مشهد انجام شد.
مواد و روش­  ها: پارامترهای میانگین دما، رطوبت، جهت و سرعت باد و مقادیر آلاینده­های مختلف به‌عنوان پارامترهای ورودی و تعداد افراد مراجعه کننده در یک روز به تفکیک جنس و سن به‌عنوان خروجی در مدل­های رگرسیون و شبکه­های عصبی پیشخور به‌کار رفته تا تأثیر گازهای مونوکسید کربن(CO)، دی­اکسید نیتروژن (NO2) و دی­اکسید گوگرد (SO2) و ذرات معلق PM2.5و PM10 بر تعداد افراد مراجعه کننده به اورژانس بررسی شود. مجموعه داده­ها شامل داده­های سازمان هواشناسی کل کشور، داده­های آلودگی هوا از سازمان هواشناسی مشهد و داده­های تعداد مراجعین روزانه بیماران قلبی به بخش اورژانس 115 مشهد بود.
یافته­ ها: مدل­های شبکه عصبی نشان می­دهند که PM10وPM2.5بیشترین تأثیر را بر افزایش میزان بیماری­های قلبی- عروقی دارندو تأثیر سایرآلاینده­ها به‌ترتیب NO2، CO و SO2 می­باشند.
نتیجه­ گیری: شبکه­های عصبی می­توانند در مدل­سازی و کشف رابطه­ پارمترهای محیطی و آلودگی­ها بر بیماران قلبی- عروقی به‌کار روند، زیرا توانایی بالایی در مدل­سازی پدیده­های غیرخطی دارند. این مدل­ها نشان می­دهند که با افزایش ذرات معلق در هوا، میزان بیماری‌های قلبی- عروقی در شهر مشهد افزایش می­یابد.

کلیدواژه‌ها

عنوان مقاله [English]

Using artificial intelligence systems to investigate relation between air pollution and acute respiratory symptoms registered at the Emergency Medical Center of Mashhad in 2017

نویسندگان [English]

  • Seyed Reza Mousavian 1
  • Aliakbar Haghdoost 2
  • Razieh Tavakoli 3

1

2 Deputy Minister of Education, Ministry of Health and Medical Education, Professor of Epidemiology and Biostatistics, Kerman University of Medical Sciences

3 Department of Electronic Engineering and Medical Engineering - Khayyam University - Mashhad - Iran

چکیده [English]

Abstract
Background and Aim: Air pollution is one of the most significant environmental problems that has a remarkable impact on the incidence of cardiovascular disease and associated mortality. It is essential to comprehend air pollution effects and the ways of emission and predict the number of patients with acute respiratory problems to eliminate and reduce air pollutants and associated mortality. This study aimed to investigate the relationship between different air pollutants and the number of cardiovascular disease 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 and the number of patients with acute respiratory problems. The inputs were average temperature, humidity, wind direction, and wind speed and the output was the number of people referred per day by gender and age. The data set used included meteorological data from the Iran Meteorological Organization, air pollution data from the Mashhad Meteorological Organization, and the number of daily referrals of heart disease patients to the emergency department of Mashhad.
Results: According to this study, the most effective air pollutants in Mashhad were PM2.5 and PM10, followed by NO2, CO, and SO2, respectively.
Conclusion: Neural networks can be applied in the modeling of the relationship between environmental parameters and cardiovascular disease patients because they have a high ability to model nonlinear phenomena. These models show that the more airborne particles, the more rate of cardiovascular diseases in Mashhad

کلیدواژه‌ها [English]

  • Air Pollution
  • Acute Respiratory Diseases
  • Artificial Neural Networks
  • Regression
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