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- Title
Estimation of myocardial infarction death in Iran: artificial neural network.
- Authors
Asghari-Jafarabadi, Mohammad; Gholipour, Kamal; Khodayari-Zarnaq, Rahim; Azmin, Mehrdad; Alizadeh, Gisoo
- Abstract
<bold>Background: </bold>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).<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusion: </bold>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.
- Subjects
IRAN; ARTIFICIAL neural networks; MYOCARDIAL infarction; DEATH rate; DISEASE risk factors; COMMUNITIES
- Publication
BMC Cardiovascular Disorders, 2022, Vol 22, Issue 1, p1
- ISSN
1471-2261
- Publication type
journal article
- DOI
10.1186/s12872-022-02871-8