International Journal of Database Theory and Application Vol.10, No.1 (2017), pp.197-206 http://dx.doi.org/10.14257/ijdta.2017.10.1.18 ISSN: 2005-4270 IJDTA Copyright ⓒ 2017 SERSC Using Decision Tree Data Mining Algorithm to Predict Causes of Road Traffic Accidents, its Prone Locations and Time along Kano –Wudil Highway L. J. Muhammad 1 , Sani Salisu 2 , Atomsa Yakubu 3 , Yusuf Musa Malgwi 4 , Elrufai Tijjani Abdullahi 5 , I .A. Mohammed 6 and Nuhu Abdul’alim Muhammad 7 1, 3,7 Mathematics & Computer Science Department, Federal University, Kashere, 2 Computer Science Department, Federal University, Dutse , Computer Science 4,5 Department, Modibbo Adama University of Technology, Yola Department of 6 Public Administration, Hassan Usman Katshina Polytechnic, Katsina, Nigeria , , 7 Department of Computer Science, Yobe State University, Damaturu, Yobe State, Nigeria mljtech@gmail.com 1 , sani.salisu@fud.edu.ng 2 , au.nlaro@gmail.com 3 yumalgwi@yahoo.com 4 tijjayrufai@gmail.com 5 ,ibrahimsallau@gmail.com 6 ,elgurama1989@gmail.com 7 Abstract Road traffic accidents, the inadvertent crash involving at least one motor vehicle, occurring on a road open to public circulation, in which at least one person is injured or killed; intentional acts (murder, suicide) and natural disasters excluded, is indisputably one of the most frequent and most damaging calamities bedeviling human societies, in particular, Nigeria, today. It is therefore, of paramount importance to seek to identify the root causes of road traffic accidents in order to proffer mitigating solutions to address the menace. This research, aimed at predicting the likely causes of road accidents, its prone locations and time along Kano– Wudil highway in order to take all necessary counter measures is a step forward in this direction. In this study data mining decision tree algorithm was used to predict the causes of the accidents, its prone locations and time along Kano – Wudil Highway that links Kano State to Wudil Local Government Area Kano State for effective decision making. Keywords: Accident, Data mining, Decision tree, Id3 tree, Algorithm 1. Introduction Road Traffic Accidents killed more than 1.2 million people, and injured between 20 and 50 million others in 2004, thereby becoming the ninth most common cause of death in that year. Road traffic accidents remains among the most central public health problems in the world. A tragic fact is that among the young people aged between 15 and 29 years, road traffic accident is one of the most common causes of death worldwide [3]. The incidence of fatal road accidents in Nigeria is phenomenal. Trend analysis of fatal road accidents between June 2006 and May 2014 using Nigeria Watch database shows that 15,090 lives were lost to fatal road accidents in 3,075 events. The highest fatality occurred in 2013 (2,061 deaths), a 2.8% increase from the 2012 record of 1,652 deaths. However, the figures were rising again in 2014, with fatality records of 964 deaths between January and May 2014 [2]. Nigeria is ranked second-highest in the rate of road accidents among 193 countries of the world. Aside from the Boko Haram crisis, accidents are currently by far the main most violent cause of death in Nigeria. The World Health Organization (WHO) adjudged Nigeria the most dangerous country in Africa with 33.7 deaths per 100,000 population