New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes

Abstract

The purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for risk factors and confirming the predictive power of previously recognized ones. We used three-dimensional motion analysis and physical data from 314 young basketball and floorball players (48.4% males, 15.72±1.79 yr, 173.34±9.14 cm, 64.65±10.4 kg). Both linear (L1-regularized logistic regression) and non-linear methods (random forest) were used to predict moderate and severe knee and ankle injuries (N=57) during three-year follow-up. Results were confirmed with permutation tests and predictive risk factors detected with Wilcoxon signed-rank-test (p<0.01). Random forest suggested twelve consistent injury predictors and logistic regression twenty. Ten of these were suggested in both models; sex, body mass index, hamstring flexibility, knee joint laxity, medial knee displacement, height, ankle plantar flexion at initial contact, leg press one-repetition max, and knee valgus at initial contact. Cross-validated areas under receiver operating characteristic curve were 0.65 (logistic regression) and 0.63 (random forest). The results highlight the difficulty of predicting future injuries, but also show that even with models having relatively low predictive power, certain predictive injury risk factors can be consistently detected.

Comparison of feature importance measures as explanations for classification models

“…The model was trained with the RandomForestClassifier function. The maximum number of features to sample at each node and the minimum number of samples required to be at a leaf node were selected with GridSearchCV using five folds and values (3,5,9,11) and (1,5,20), respectively.…”

“…To confirm the significance of the important features and achieved performance, we apply an approach introduced in [20] based on permutations tests. By shuffling the class labels in the training data we made sure that the model was not simply learning some noise in data and therefore achieving higher performance and feature importance values than the chance level [11].…”

A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players

“…Taking into account the above-reported existing methodologies and their limitations, as well the recent advances in artificial intelligence (AI), the application of machine-learning algorithms appears to be a promising approach for diagnosis and prediction in several fields, such as ACL injury [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ], and, more generally, in biomedicine approaches [ 31 , 32 , 33 ]. Focusing on ACL, diagnosis and prediction represent two correlated analyses that permit us to solve two different issues.…”

“…Similar results were obtained by applying a convolutional neural network on coronal MRI, reaching an accuracy of 96% in ACL diagnoses [ 25 ]. By moving to the use of AI as a predictive tool, Jauhieinen and colleagues demonstrated a random forest was able to detect the most predictive biomechanical factors, which were mostly related to the knee joint kinematics and kinetics, during vertical jumping tests performed by 314 basketball and football players [ 28 ]. They also assessed the difficulty in the prediction of future injuries, also confirmed by the low value of the area under receiver operator curve, lower than 0.70 for both random forest and logistic regression.…”

Sports Injury Rehabilitation Intervention Algorithm Based on Visual Analysis Technology

“…erefore, athletes' sports injury has become the main factor that continuously limits the performance improvement of high-level athletes. It requires the use of necessary sports injury detection methods to detect high-level athletes [6][7][8].…”