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The paper introduces a new dataset to assess the performance of machinelearning algorithms in the prediction of the seriousness of injury in a trafficaccident. The dataset is created by aggregating publicly available datasetsfrom the UK Department for Transport, which are drastically imbalanced withmissing attributes sometimes approaching 50\% of the overall datadimensionality. The paper presents the data analysis pipeline starting from thepublicly available data of road traffic accidents and ending with predictors ofpossible injuries and their degree of severity. It addresses the hugeincompleteness of public data with a MissForest model. The paper alsointroduces two baseline approaches to create injury predictors: a supervisedartificial neural network and a reinforcement learning model. The dataset canpotentially stimulate diverse aspects of machine learning research onimbalanced datasets and the two approaches can be used as baseline referenceswhen researchers test more advanced learning algorithms in this area.

Predicting Seriousness of Injury in a Traffic Accident: A New Imbalanced Dataset and Benchmark