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The study of traffic accidents in Zanjan Province between March 2009 and February 2016 and comparing the mathematical method to predict traffic injuries referred to the Forensic Medicine between 2017 and 2020
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Nabi Omidi1   , Heshmatolaah Asgari 2, Mohammad Reza Omidi3 , Meysam Jafari Eskandari4 |
1- Department of Management
2- Faculty of Humanities , he.asgari@gmail.com
3- Lecturer, Department of Industrial Engineering
4- Department of Industrial Engineering |
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Abstract: (4730 Views) |
| Background: Accidents cause mortality and severe damage to life and property. Heavy consequences of social, economic, cultural and human societies is seriously threatened, which is important for future studies in the field of traffic accidents.
Materials and methods: In this study, using gray system GM (1,1), gray circulation model RGM (1,1), gray model FGM (1,1), remained the same model, ARIMA time series model and the harmonic pattern, as well as the use of statistical data, by the number of victims of traffic accidents in the province between March 2009 and February 2016, the number of victims referred to the coroner province between 2017 and 2020 was predicted.
Results: MAPE for models GM, RGM, FGM, changes survivor, ARIMA and harmonics to predict the number of patients referred to the 0.10, 0.07, 0.12, 0.14, 0.13 and 0.15, respectively. It indicates a more accurate method of RGM the anticipated number of patients referred to the coroner in the province of Zanjan. The number of victims in the year of 2009 reaches to 2894 people.
Conclusion: The RGM is the best method to predict the number of disaster victims referred to legal medicine of Zanjan province, compared to other methods of case study. Reducing the number of disaster victims referred to legal medicine represents the improvement of driving tips and actions and nested in the province.
Keywords: Future of research, Forecasting, Data mining, Safety, Accidents, Forensic medicine. |
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| Keywords: Future of research, Forecasting, Data mining, Safety, Accidents, Forensic medicine |
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Full-Text [PDF 263 kb]
(1186 Downloads)
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Semi-pilot: retrospective |
Subject:
Health Information Management
Received: 2016/10/8 | Accepted: 2017/09/20 | Published: 2017/10/2
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