An evaluation of the random forest, chi-square, and mRMR feature selection methods on binary indicators of ACL injury risk

Driven by the increase in applications of Machine Learning in the medical industry and in biomedical engineering, we sought to aid in the creation of predictive models within the sports medicine field. In this paper, we look at a set of binary data of extrinsic factors that contribute to anterior cruciate ligament (ACL) injuries. We use multiple algorithms to select the most relevant feature that can be used to aid in making a predictive model. A dataset of binary data was compiled from 12639 sports matches. As a result, the extent of each condition was not able to be measured. Nonetheless, the data is used and is run through the Minimum Redundancy Maximum Relevance (mRMR), Chi-Square, and Random Forest processes. We also weigh the features by using a shallow artificial neural network with an input and output layer. Based on the results, the feature that was selected the most and was highest weighted was the condition of having low rainfall before the sports match.