Open AccessTraffic Accidents Severity Prediction using Support Vector Machine Models
Author(s) -
Zeinab Farhart*,
Ali Karouni,
Bassam Daya,
Pierre Chauvet,
Nizar Hmadeh
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f4393.059720
Subject(s) - support vector machine , radial basis function , computer science , kernel (algebra) , machine learning , function (biology) , artificial intelligence , data mining , artificial neural network , mathematics , combinatorics , evolutionary biology , biology
In recent years, road traffic accidents (RTA) have become one of the highest national health concerns worldwide. RTA have become the leading cause of losing lives among children and youth. Recent studies have proven that Data Mining Techniques can break down the complexity that prevails between RTA and corresponding factors. In this paper, Support Vector Machine (SVM) based on Radial basis function (RBF) and Linear Kernel Function is applied to predict fatal road accidents in Lebanon. The experimental results reveal that SVM using RBF give the highest accuracy (86%) and the best AUC (86.6%). The obtained decision-making model claims to tackle the fatal RTA phenomenon.