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Decision Tree Model for Non-Fatal Road Accident Injury

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@article{IJASEIT1110,
   author = {Fatin Ellisya Sapri and Nur Shuhada Nordin and Siti Maisarah Hasan and Wan Fairos Wan Yaacob and Syerina Azlin Md Nasir},
   title = {Decision Tree Model for Non-Fatal Road Accident Injury},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {1},
   year = {2017},
   pages = {63--70},
   keywords = {road accident injury severity; logistic regression; decision tree; CART; LR main},
   abstract = {Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1110},
   doi = {10.18517/ijaseit.7.1.1110}
}

EndNote

%A Sapri, Fatin Ellisya
%A Nordin, Nur Shuhada
%A Hasan, Siti Maisarah
%A Wan Yaacob, Wan Fairos
%A Md Nasir, Syerina Azlin
%D 2017
%T Decision Tree Model for Non-Fatal Road Accident Injury
%B 2017
%9 road accident injury severity; logistic regression; decision tree; CART; LR main
%! Decision Tree Model for Non-Fatal Road Accident Injury
%K road accident injury severity; logistic regression; decision tree; CART; LR main
%X Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1110
%R doi:10.18517/ijaseit.7.1.1110
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 1
%@ 2088-5334

IEEE

Fatin Ellisya Sapri,Nur Shuhada Nordin,Siti Maisarah Hasan,Wan Fairos Wan Yaacob and Syerina Azlin Md Nasir,"Decision Tree Model for Non-Fatal Road Accident Injury," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, pp. 63-70, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.1.1110.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sapri, Fatin Ellisya
AU  - Nordin, Nur Shuhada
AU  - Hasan, Siti Maisarah
AU  - Wan Yaacob, Wan Fairos
AU  - Md Nasir, Syerina Azlin
PY  - 2017
TI  - Decision Tree Model for Non-Fatal Road Accident Injury
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 1
Y2  - 2017
SP  - 63
EP  - 70
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - road accident injury severity; logistic regression; decision tree; CART; LR main
N2  - Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1110
DO  - 10.18517/ijaseit.7.1.1110

RefWorks

RT Journal Article
ID 1110
A1 Sapri, Fatin Ellisya
A1 Nordin, Nur Shuhada
A1 Hasan, Siti Maisarah
A1 Wan Yaacob, Wan Fairos
A1 Md Nasir, Syerina Azlin
T1 Decision Tree Model for Non-Fatal Road Accident Injury
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 1
YR 2017
SP 63
OP 70
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 road accident injury severity; logistic regression; decision tree; CART; LR main
AB Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1110
DO  - 10.18517/ijaseit.7.1.1110