Abstract:
Motorbike accident is most dangerous among all accident on road. Many people dies because
of bike accident. Motorbike accident damages both public and private property. A victim’s
family have to spent a big amount of money for the treatment of the biker. Sometimes their
family falls into financial crisis to arrange money for treatment of the victim. Bikers need to be
aware about accident and should avoid the facts that causes accident. In this study different
machine learning algorithm has been employed to predict the severity of the motorbike
accident. Then we collect data depending on those criteria, such as speeding, overtaking,
turning, bike fitness issues, speed-breakers without signs, unsafe lane changes, talking with a
passenger, and highways without road dividers, among others. We only collect information
from bikers who have been in an accident. We processed all of the data once it was collected
and developed a processed dataset. On our processed dataset, we used machine learning
techniques. Machine learning has been employed in various prediction and detection systems
since their inception. Random Forest, Multilayer Perception (MLP), Decision Tree, Logistic
Regression, k-Nearest Neighbors (KNN), AdaBoost, GNB, SVM with RBF Kernel, Linear
SVC and Gradient Boosting are just a few of the techniques we utilize. MLP offered the
greatest results in terms of accuracy. It showed an accuracy of 83.10%. And again MLP
performs better in terms of sensitivity, specificity, F1-Score and precision.
