Support vector machine with principle component analysis for road traffic crash severity classification
Road traffic crash (RTC) is one among the leading causes of death in the world, including Nigeria. It also turns many victims completely disabled and generally affected the socio-economic development in the society. In this paper, we proposed to predict the road crash severity injuries in Nigeria by...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/90982/1/Nor%20HaizanRadzi2019_SupportVectorMachinewithPrinciple.pdf http://eprints.utm.my/id/eprint/90982/ http://www.dx.doi.org/10.1088/1757-899X/551/1/012068 |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | Road traffic crash (RTC) is one among the leading causes of death in the world, including Nigeria. It also turns many victims completely disabled and generally affected the socio-economic development in the society. In this paper, we proposed to predict the road crash severity injuries in Nigeria by identifying the most significant contributory factors using Principal Component Analysis with Support Vector Machine (SVM) used for classification algorithm. Road crash data from year 2013-2015 obtained from Federal Road Safety Corps Nigeria is used in this study. The result shows that and increased to 87% compared to 82% without feature selection. |
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