Predicting Severity Level of Road Traffic Accidents in Oromiya East Shewa Zone using Iterative Dichotomiser3
Anusuya Ramasamy1, Shambel Dechasa2, Addisu Mulugeta3

1Dr. Anusuya Ramasamy*, Assistant Professor, Faculty of Computing and Software Engineering, AMIT, Arbaminch University, Ethiopia.
2Mr. Shambel Dechasa *, Lecturer, Oromia State University, Batu/Zway, Oromia, Ethiopia, Dean College of Science and Technology.
3Mr.Addisu Mulugeta *, Lecture, Faculty of Computing and Software Engineering, AMIT, Arbaminch University, Ethiopia. 

Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2262-2267 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2855059120/2020©BEIESP | DOI: 10.35940/ijrte.A2855.059120
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Abstract: Abstract: – Highway traffic accidents are a main community health problem unease ensuing millions fatalities and million serious injuries in the world each year. In the developing country like Ethiopia, is also the victim of road traffic accident or crush causing deaths, property damage and serious injuries. In order to analyses severity level of road traffic accidents, data is important to find out factors that are related to fatal, grievous, minor and non- injuries to gauge a fixed variables that contributes towards forecast the severity level of road traffic crashes. A lane traffic stream pound or impact happens when a vehicle slams into another vehicle, passerby, creature, or geological or building obstruction and result in injury, property harm, and lethal/demise. Path traffic control framework is, where basic information about the squash is recorded and saved for looming use. Expending that information the proposed examination have been extricated the contributing elements of street auto collision and create prescient model to foresee seriousness level for street car crash, wounds and fatalities utilizing information mining methods.. The main task of research is to make known the applicability of data mining techniques in emerging a model to support road traffic accident brutality analysis in preventing and extracting patterns that are corresponding with road accident in different ways of presentation methods. The road traffic accident historical data ,obtained from traffic Oromia police commission of East Shewa Zone, Oromia and police commission of Federal government of Ethiopia.
Keywords: Association Rule Mining, Classification, Data mining, ID3, Prediction, Random Forest, Random tree and Naïve Bayes, Road Traffic Accident.
Scope of the Article: Classification