Investigating artificial neural networks versus regression models in predicting Myocardial Infarction (MI) mortality based on climatic elements in Sanandaj city, Iran

Investigating artificial neural networks versus regression models in predicting Myocardial Infarction (MI) mortality based on climatic elements in Sanandaj city, Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating artificial neural networks versus regression models in predicting Myocardial Infarction (MI) mortality based on climatic elements in Sanandaj city, Iran Bromand Salahi, Seyed Asaad Hosseini, Kaweh Mohammadpour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2457607/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the present study, to analyze the relationship between mortality due to Myocardial Infarction (MI) with climatic parameters and its prediction, the ability of artificial neural network models, and linear and nonlinear regression in Sanandaj was evaluated. The study population in this study is the total number of monthly deaths due to myocardial infarction (MI) in Sanandaj city and also the data related to the climatic variables of Sanandaj synoptic station during the statistical period 2014–2018. Variables such as mean monthly temperature, mean minimum, and maximum monthly temperature, average monthly minimum and maximum station air pressure (QFE), total hours of sunshine, and several days with minimum temperature, equal to or below zero and the output of the models is the total number of monthly deaths due to MI in Sanandaj city. The results showed that there is a nonlinear relationship between the total number of monthly deaths due to MI and climatic parameters in Sanandaj, Which can be measured and predicted only by an artificial neural network (ANNs) model, and multiple linear and nonlinear regression models do not have the necessary efficiency in this field. Climatic Parameters Prediction Myocardial Infarction Sanandaj City Regression Model Artificial Neural Network Modelling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2457607","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":166785821,"identity":"4e0dae05-bed9-4a03-9825-4973d7840c7a","order_by":0,"name":"Bromand Salahi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACAwjFzMzG3nyAgbGBBC3sfDzHEkjTwi8n4WNAnBZz6cNHN/zcYS3NJsHzTeLnDhs5BnagCD4tln1paTd7z6Qbs0n3bpPsPZNmzMCTlnYDr8PO8Jjd4G07nMwmc3abBJCR2CABFCGk5ebftsP1bRI5zyT/EqvlNtBwZjaJHDZpomyx7GFLuy3bls7MxnPM2Fq2Lc2YjZBfzHmYj91822bNLN/e/BDIsJHjZz98DK8WZMAiASLZiFUOAswfSFE9CkbBKBgFIwcAALefR+WmdXdKAAAAAElFTkSuQmCC","orcid":"","institution":"University of Mohaghegh Ardabili. 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