# Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques
## 2 Related Works
### Previous Studies  
The first study in the KSA used categorical types of attributes in a decision tree (CHAID and J48) and a probabilistic model, achieving 98% accuracy [25]. However, the classifiers were insufficient to represent the minority groups, i.e., injury and death. Driver distraction was found to be an important cause of injury severities. Two studies in the United Arab Emirates (UAE) used the same data and applied a multilayer perceptron (MLP) and ordered probit, with four classes targeted [26; 27]. The overall prediction accuracy of the decision tree (J48) model after resampling the training set was 88.08% and 0.93 area under (AUC) of the ROC curve. The studies found that age between 18 and 30 years, gender (male), and collision type were the most important factors associated with fatal severity. The literature identifies the need for advanced approach applications on severity prediction modeling in the Gulf region. [PERSON] et al. [28] developed a popular ML algorithm known as the random forest (RF) classifier, which has been frequently used for predicting the severity of RTAs. The random forest always outperformed the decision trees and other algorithms [29; 30; 31; 32; 20; 33] Random Forest is an effective approach for predicting road traffic crash injury severity [34]. It was also adopted by [PERSON] et al. in its latest application in the field of engineering [35]. [PERSON] et al. [36] studied motorcycle crashes on two-lane highways with four-level classes of data, and the study relied on the literature to construct a binary category response. They found that the posted speed limit is the most important predictor, followed by age, highway functional class, and speed compliance. However, the authors stated that there was a possibility of bias identification, as the posted speed limit was the only continuous variable. [PERSON] et al. [37] were among the first to apply extreme gradient boosting trees (XGBoost), a relatively new classifier, to severity prediction [38]. The data was imbalanced, so the synthetic minority oversampling technique (SMOTE), a popular technique developed by [39] to handle imbalanced datasets, was used for the minority classes. Despite utilizing SMOTE, the four-class accuracy of the classifiers did not meet expectations, and the multi-class label was switched to binary classes. Over the last five years, crash severity research has witnessed a revolution in terms of ML techniques due to the development of data science approaches. [PERSON] and [PERSON] [40] developed a framework based on a deep neural network (DNN) and applied ImageNet competition to improve the accuracy of the predictions using a stacking technique, a general procedure developed by [41] to enhance predictive accuracy by combining several predictive algorithms. The stacked model outperformed the other classifiers [10; 22]. [PERSON] et al. used Shapley Additive explanations (SHAP) to interpolate feature importance, which is a recent approach developed by [PERSON] et al. [42]. The SHAP value impact on a model gives a high value for collision type, person count, and vehicle count. The authors in [43] adopted a light gradient boosting machine (LightGBM), novel natural gradient boosting (NGBoost), categorical boosting (CatBoost), and AdaBoost to predict two class responses. LightGBM outperformed the others regardingaccuracy and ROC curve. Feature importance was determined via SHAP. Injury severity was found to be affected by month, age, cause, and collision type. Random forest (RF) is an effective approach for predicting RTA injury severity, followed by support vector machine, decision tree, and k-nearest neighbor [34]. However, DNN has been adopted in recent years and its results show promising accuracy [40; 44]. Furthermore, the stacking and boosting techniques [45; 46] are also accurate and distinguishable from a label perspective. Recently, extreme gradient boosting (XGBoost) outperformed random forest in a study conducted by [PERSON] et al. [47; 48; 49; 50].