Estimation of myocardial infarction death in Iran: artificial neural network

Abstract Background Examining past trends and predicting the future helps policymakers to design effective interventions to deal with myocardial infarction (MI) with a clear understanding of the current and future situation. The aim of this study was to estimate the death rate due to MI in Iran by a...

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Main Authors: Mohammad Asghari-Jafarabadi, Kamal Gholipour, Rahim Khodayari-Zarnaq, Mehrdad Azmin, Gisoo Alizadeh
Format: Article
Language:English
Published: BMC 2022-10-01
Series:BMC Cardiovascular Disorders
Subjects:
Online Access:https://doi.org/10.1186/s12872-022-02871-8
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author Mohammad Asghari-Jafarabadi
Kamal Gholipour
Rahim Khodayari-Zarnaq
Mehrdad Azmin
Gisoo Alizadeh
author_facet Mohammad Asghari-Jafarabadi
Kamal Gholipour
Rahim Khodayari-Zarnaq
Mehrdad Azmin
Gisoo Alizadeh
author_sort Mohammad Asghari-Jafarabadi
collection DOAJ
description Abstract Background Examining past trends and predicting the future helps policymakers to design effective interventions to deal with myocardial infarction (MI) with a clear understanding of the current and future situation. The aim of this study was to estimate the death rate due to MI in Iran by artificial neural network (ANN). Methods In this ecological study, the prevalence of diabetes, hypercholesterolemia over 200, hypertension, overweight and obesity were estimated for the years 2017–2025. ANN and Linear regression model were used. Also, Specialists were also asked to predict the death rate due to MI by considering the conditions of 3 conditions (optimistic, pessimistic, and probable), and the predicted process was compared with the modeling process. Results Death rate due to MI in Iran is expected to decrease on average, while there will be a significant decrease in the prevalence of hypercholesterolemia 1.031 (− 24.81, 26.88). Also, the trend of diabetes 10.48 (111.45, − 132.42), blood pressure − 110.48 (− 174.04, − 46.91) and obesity and overweight − 35.84 (− 18.66, − 5.02) are slowly increasing. MI death rate in Iran is higher in men but is decreasing on average. Experts' forecasts are different and have predicted a completely upward trend. Conclusion The trend predicted by the modeling shows that the death rate due to MI will decrease in the future with a low slope. Improving the infrastructure for providing preventive services to reduce the risk factors for cardiovascular disease in the community is one of the priority measures in the current situation.
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spelling doaj.art-dc1b3e8efde744f3bc755f98e8c0a18a2022-12-22T03:38:21ZengBMCBMC Cardiovascular Disorders1471-22612022-10-012211810.1186/s12872-022-02871-8Estimation of myocardial infarction death in Iran: artificial neural networkMohammad Asghari-Jafarabadi0Kamal Gholipour1Rahim Khodayari-Zarnaq2Mehrdad Azmin3Gisoo Alizadeh4Cabrini Research, Cabrini HealthTabriz Health Service Management Research Center, School of Management and Medical Informatics, Tabriz University of Medical SciencesDepartment of Health Policy and Management, School of Management and Medical Informatics, Tabriz University of Medical SciencesNon-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical SciencesTabriz Health Service Management Research Center, School of Management and Medical Informatics, Tabriz University of Medical SciencesAbstract Background Examining past trends and predicting the future helps policymakers to design effective interventions to deal with myocardial infarction (MI) with a clear understanding of the current and future situation. The aim of this study was to estimate the death rate due to MI in Iran by artificial neural network (ANN). Methods In this ecological study, the prevalence of diabetes, hypercholesterolemia over 200, hypertension, overweight and obesity were estimated for the years 2017–2025. ANN and Linear regression model were used. Also, Specialists were also asked to predict the death rate due to MI by considering the conditions of 3 conditions (optimistic, pessimistic, and probable), and the predicted process was compared with the modeling process. Results Death rate due to MI in Iran is expected to decrease on average, while there will be a significant decrease in the prevalence of hypercholesterolemia 1.031 (− 24.81, 26.88). Also, the trend of diabetes 10.48 (111.45, − 132.42), blood pressure − 110.48 (− 174.04, − 46.91) and obesity and overweight − 35.84 (− 18.66, − 5.02) are slowly increasing. MI death rate in Iran is higher in men but is decreasing on average. Experts' forecasts are different and have predicted a completely upward trend. Conclusion The trend predicted by the modeling shows that the death rate due to MI will decrease in the future with a low slope. Improving the infrastructure for providing preventive services to reduce the risk factors for cardiovascular disease in the community is one of the priority measures in the current situation.https://doi.org/10.1186/s12872-022-02871-8EstimationDeath rateMyocardial infarctionArtificial neural networkIran
spellingShingle Mohammad Asghari-Jafarabadi
Kamal Gholipour
Rahim Khodayari-Zarnaq
Mehrdad Azmin
Gisoo Alizadeh
Estimation of myocardial infarction death in Iran: artificial neural network
BMC Cardiovascular Disorders
Estimation
Death rate
Myocardial infarction
Artificial neural network
Iran
title Estimation of myocardial infarction death in Iran: artificial neural network
title_full Estimation of myocardial infarction death in Iran: artificial neural network
title_fullStr Estimation of myocardial infarction death in Iran: artificial neural network
title_full_unstemmed Estimation of myocardial infarction death in Iran: artificial neural network
title_short Estimation of myocardial infarction death in Iran: artificial neural network
title_sort estimation of myocardial infarction death in iran artificial neural network
topic Estimation
Death rate
Myocardial infarction
Artificial neural network
Iran
url https://doi.org/10.1186/s12872-022-02871-8
work_keys_str_mv AT mohammadasgharijafarabadi estimationofmyocardialinfarctiondeathiniranartificialneuralnetwork
AT kamalgholipour estimationofmyocardialinfarctiondeathiniranartificialneuralnetwork
AT rahimkhodayarizarnaq estimationofmyocardialinfarctiondeathiniranartificialneuralnetwork
AT mehrdadazmin estimationofmyocardialinfarctiondeathiniranartificialneuralnetwork
AT gisooalizadeh estimationofmyocardialinfarctiondeathiniranartificialneuralnetwork