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Predicting Running Injuries with Machine Learning Models
Elgin V. | Summer 2022 |
workspace_premium Published in: Journal of Student Research open_in_new

Is it possible to predict running injuries with only a dataset and machine learning models? This paper explores this question by using classification models, including the Logistic Regression model and the Random Forest Classifier model.

Is it possible to predict running injuries with only a dataset and machine learning models? This paper explores this question by using classification models, including the Logistic Regression model and the Random Forest Classifier model. In the dataset used, ten features were taken into account when predicting running injuries. With slight modifications, the Weighted Logistic Regression and over and down-sampling Random Forest Classifier models were used to mitigate the imbalance in the dataset. The results suggested that the best model was Weighted Logistic Regression and that the best score metric to take into account was the F beta score.

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Published Paper
Elgin V.
Joseph Vincent
Aerospace Engineering PhD Candidate at Stanford

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