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Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents
- Source :
- PLoS ONE, Vol 17, Iss 2, p e0262941 (2022), PLoS ONE
- Publication Year :
- 2022
- Publisher :
- Public Library of Science (PLoS), 2022.
-
Abstract
- To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machine learning approaches have become an important aspect in predicting the severity of road traffic injuries. The paper presents a hybrid feature selection-based machine learning classification approach for detecting significant attributes and predicting injury severity in single and multiple-vehicle accidents. To begin, we employed a Random Forests (RF) classifier in conjunction with an intrinsic wrapper-based feature selection approach called the Boruta Algorithm (BA) to find the relevant important attributes that determine injury severity. The influential attributes were then fed into a set of four classifiers to accurately predict injury severity (Naive Bayes (NB), K-Nearest Neighbor (K-NN), Binary Logistic Regression (BLR), and Extreme Gradient Boosting (XGBoost)). According to BA’s experimental investigation, the vehicle type was the most influential factor, followed by the month of the year, the driver’s age, and the alignment of the road segment. The driver’s gender, the presence of a median, and the presence of a shoulder were all found to be unimportant. According to classifier performance measures, XGBoost surpasses the other classifiers in terms of prediction performance. Using the specified attributes, the accuracy, Cohen’s Kappa, F1-Measure, and AUC-ROC values of the XGBoost were 82.10%, 0.607, 0.776, and 0.880 for single vehicle accidents and 79.52%, 0.569, 0.752, and 0.86 for multiple-vehicle accidents, respectively.
- Subjects :
- Computer and Information Sciences
Asia
Epidemiology
Science
Highways
Transportation
Research and Analysis Methods
Severity of Illness Index
Civil Engineering
Machine Learning
Geographical Locations
Machine Learning Algorithms
Artificial Intelligence
Medicine and Health Sciences
Humans
Pakistan
Public and Occupational Health
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Traumatic Injury Risk Factors
Accidents, Traffic
Traffic Safety
Bayes Theorem
Transportation Infrastructure
Roads
Logistic Models
ROC Curve
Area Under Curve
Medical Risk Factors
Road Traffic Collisions
Physical Sciences
People and Places
Wounds and Injuries
Engineering and Technology
Medicine
Safety
Mathematics
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 17
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....f48019ee8c485dc7e66d657a05d5c7fc