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Machine Learning Based Model for Predicting Head Injury Criterion (HIC)
Published March 31, 2020 by The Stapp Association in United States
This content contains downloadable datasetsAnnotation ability available
The objective of this study is to develop a machine learning based predictive model from the available crash test data and use it for predicting injury metrics. In this study, a model was developed for predicting the head injury criterion, HIC15, using pre-test features (vehicle, test, occupant and restraint related). This problem was solved as a classification task, in which HIC15 with a threshold of 700 was divided into three classes i.e. low, medium and high. Crash test data was collected from the NHTSA database and was split into training and test datasets. Predictive models were developed from the training dataset using cross-validation while the test dataset was only used at the final step to evaluate the chosen predictive model. A logistic regression based predictive model was chosen as it demonstrated minimal overfitting and gave the highest F1 score (0.81) on the validation dataset. This chosen model gave a F1 score of 0.82 on the test (new/unseen) dataset.
CitationHasija, V. and Takhounts, E., "Machine Learning Based Model for Predicting Head Injury Criterion (HIC)," SAE Technical Paper 2019-22-0016, 2020, https://doi.org/10.4271/2019-22-0016.
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