| Literature DB >> 36207680 |
Mohammad Asghari-Jafarabadi1,2,3, Kamal Gholipour4, Rahim Khodayari-Zarnaq5, Mehrdad Azmin6, Gisoo Alizadeh7,8.
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).Entities:
Keywords: Artificial neural network; Death rate; Estimation; Iran; Myocardial infarction
Mesh:
Year: 2022 PMID: 36207680 PMCID: PMC9547455 DOI: 10.1186/s12872-022-02871-8
Source DB: PubMed Journal: BMC Cardiovasc Disord ISSN: 1471-2261 Impact factor: 2.174
Profile of participants in predicting the death rate due to myocardial infarction
| No | Gender | Degree of education | Field of Study | Job | |
|---|---|---|---|---|---|
| Male | Female | ||||
| 1 | * | Ph.D | Epidemiology | Science committee | |
| 2 | * | Ph.D | Health economics | Science committee | |
| 3 | * | Ph.D | Health management services | Science committee | |
| 4 | * | Ph.D. student | Health policy making | Student | |
| 5 | * | Ph.D | Health policy making | Science committee | |
| 6 | * | Ph.D | Health promotion | Science committee | |
| 7 | * | Ph.D. student | Epidemiology | Student | |
| 8 | * | Ph.D | Elderly science | Science committee | |
| 9 | * | Ph.D | Health services management | Science committee | |
| 10 | * | Ph.D | Elderly science | Science committee | |
| 11 | * | Ph.D. student | Health management services | Student | |
| 12 | * | Ph.D | Health policy making | Science committee | |
| 13 | * | Ph.D. student | Health policy making | Student | |
Fig. 1Prevalence of risk factors, Age-Standard (percentage)
Fig. 2MI deaths—Age-Standard (per 100,000)
Fig. 3Comparison of predicted deaths in different scenarios due to myocardial infarction in 100,000 populations in women—Age-Standard
Fig. 4Comparison of death rates due to myocardial infarction in 100,000 population in men- Age-Standard
Results of multiple Regression model of the predictors of MI Death rate
| Variable | Male | Female | ||||||
|---|---|---|---|---|---|---|---|---|
| Coefficient | Coefficient 95% confidence interval | Coefficient | Coefficient 95% confidence interval | |||||
| Upper limit | Low limit | Upper limit | Low limit | |||||
| Age | 6.57 | 61.32 | − 48.17 | 0.81 | 34.70 | − 42.72 | 112.1 | 0.37 |
| Diabetes | 34.31 | − 39.17 | 20.86 | 0.193 | 53.19 | 7.32 | 99.06 | 0.023 |
| blood pressure | 46.91 | 30.13 | 63.69 | < 0.001 | 23.55 | 9.15 | 37.95 | 0.001 |
| Hypercholesterolemia > 200 | − 43.89 | − 57.68 | − 30.10 | < 0.001 | − 75.26 | − 83.83 | − 66.68 | < 0.001 |
| Overweight and obesity | − 39.39 | − 49.15 | − 29.63 | < 0.001 | − 25.38 | − 35.77 | − 15 | < 0.001 |
| Age* | 771.09 | 604.72 | 937.46 | < 0.001 | 1037.65 | 869.82 | 1205.48 | < 0.001 |
| Prevalence of diabetes | 10.48 | − 11.45 | 13.42 | 0.86 | 16.63 | − 65.73 | 99 | 0.69 |
| Prevalence of hypertension | − 110.48 | − 174.04 | − 46.91 | 0.001 | − 129.03 | − 168.14 | − 89.92 | < 0.001 |
| Prevalence of hypercholesterolemia > 200 | 1.31 | − 24.81 | 26.88 | 0.937 | 17.79 | − 1.82 | 37.42 | 0.075 |
| Prevalence of overweight and obesity | − 35.84 | − 52.02 | − 18.66 | < 0.001 | − 13.21 | − 32.56 | 6.12 | 0.178 |
Dependent variable: death rate in 100,000
*Age is classified into 5-year groups (1 = 25–29 years old, 13 ≥ 85 years old)