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Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5\u00a0years, the mean concentrations of PM<jats:sub>10<\/jats:sub>, SO<jats:sub>2<\/jats:sub>, O<jats:sub>3<\/jats:sub>, NO<jats:sub>2<\/jats:sub>, and CO were 98.48\u00a0\u03bcg\u00a0m<jats:sup>\u22123<\/jats:sup>, 8.57\u00a0ppm, 19.66\u00a0ppm, 18.14\u00a0ppm, and 4.07\u00a0ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM<jats:sub>10<\/jats:sub> (R\u2009=\u20090.62). The presence of SO<jats:sub>2<\/jats:sub> and NO<jats:sub>2<\/jats:sub> can be dependent on meteorological parameters (R\u2009=\u20090.48). Despite there was a positive correlation between age and CD (p\u2009=\u20090.001), the highest correlation was detected between SO<jats:sub>2<\/jats:sub> and CD (R\u2009=\u20090.4). The annual variation trend of SO<jats:sub>2<\/jats:sub>, NO<jats:sub>2<\/jats:sub>, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O<jats:sub>3<\/jats:sub> variation rate at lag\u2009=\u20090. On the other hand, SO<jats:sub>2<\/jats:sub> has been the most effective contaminant in CD patient admissions in hospitals (R\u2009=\u20090.45). In the monthly database classification, SO<jats:sub>2<\/jats:sub> and NO<jats:sub>2<\/jats:sub> were the most prominent factors in the CD (R\u2009=\u20090.5). The multivariate linear regression model also showed that CO and SO<jats:sub>2<\/jats:sub> were significant contaminants in the number of hospital admissions (R\u2009=\u20090.46, p\u2009=\u20090.001) that both pollutants were a function of air temperature (p\u2009=\u20090.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO<jats:sub>2<\/jats:sub>, O<jats:sub>3<\/jats:sub>, and SO<jats:sub>2<\/jats:sub>, respectively. Thus, the R<jats:sub>all<\/jats:sub> rate for these structures was 0.78\u20130.83. In these structures, according to the autocorrelation of error in lag\u2009=\u20090, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5\u00a0years' time series.<\/jats:p>","DOI":"10.1038\/s41598-021-94925-8","type":"journal-article","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T10:03:20Z","timestamp":1629453800000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran"],"prefix":"10.1038","volume":"11","author":[{"given":"Mahrokh","family":"Jalili","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Hassan","family":"Ehrampoush","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Mokhtari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Asghar","family":"Ebrahimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faezeh","family":"Mazidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fariba","family":"Abbasi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Karimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"94925_CR1","doi-asserted-by":"crossref","unstructured":"Abbasi, F. et al. 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