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Table 5 Study results characteristics

From: Machine learning methods in sport injury prediction and prevention: a systematic review

Authors, Year Performance Measures (+ Values for Best ML Model) Predictive Performance of ML Methods Measures of Feature Importance Most Important Injury Predictors
Ayala et al., 2019 [2] AUC (0.873), Sensitivity (77.8%), Specificity (83.8%) An alternating decision tree, combined with synthetic minority oversampling and boosting gave the best results The frequency with which each of the features appears across the tree classifiers Sleep Quality
Carey et al., 2018 [10] (Median) AUC (all below 0.65), Sensitivity, Specificity, Precision, False Disovery Rate, Likelihood Ratios The proposed ML models perform only marginally better than would be expected by random chance NR NR
López-Valenciano et al., 2018 [25] AUC (0.747), Sensitivity (65.5%), Specificity (79.1%) An alternating decision tree, combined with synthetic minority oversampling and boosting gave the best results The frequency with which each of the features appears across the tree classifiers sport devaluation, history of muscle injury in last season
McCullagh et al., 2013 [27] Accuracy (82.9%), Sensitivity (94.5%), Specificity (81.1%) Indication that Artificial Neural Networks are able to derive meaningful information from the vast amount of data available to assist in the injury prediction process NR NR
Oliver et al., 2020 [32] AUC (0.663), Sensitivity (55.6%), Specificity (74.2%) The machine learning model provided improved sensitivity to predict injury The frequency with which each of the features appears across the tree classifiers interactions of asymmetry, knee valgus angle and body size
Rodas et al., 2019 [34] Accuracy (52%), Sensitivity (75%), Specificity (23%) There is low prediction potential for presence or absence of tendinopathy The number of times that a feature (genetic predictor) received a non-zero coefficient in the LASSO analysis rs10477683 in the fibrillin 2 gene was the most robust SNP (single-nucleotide polymorphism)
Rommers et al., 2020 [35] F1-score (85%), Sensitivity (85%), Precision (85%) It is possible to predict injury with high accuracy SHAP (SHapley Additive exPlanations) summary plot Higher predicted age at PHV (peak height velocity), longer legs, higher body height, lower body fat percentage
Rossi et al., 2018 [36] (Mean) AUC (0.76), F1-score (64%), Sensitivity (80%), Specificity (87%) Precision (50%), Negative Predicted Value (96%) The single Decision tree performs best in terms of precision Mean decrease in Gini coefficient Previous injury (exponential weighted moving average), total distance (monotony of workload feature) and high-speed running distance (exponential weighted moving average)
Ruddy et al., 2018 [38] (Median) AUC (0.58, 0.57 and 0.52) Eccentric hamstring strength, age, and previous hamstring strain injury (HSI) data cannot be used to identify athletes at an increased risk of HSI with any consistency NR NR
Thornton et al., 2017 [41] AUC (0.74, 0.65, 0.64 and 0.64) Machine learning techniques can appropriately monitor injury risk amongst professional team sport athletes Number of times that each feature appears in the ensemble of decision trees The relative importance of each training load variable varied for each player
Whiteside et al., 2016 [46] Accuracy (75%), Sensitivity (74%), Specificity (75%), Precision (75%), False Omission Rate (26%) Machine learning models can predict future ulnar collateral ligament surgeries with high accuracy The frequency with which each feature appeared in the optimized models in the fivefold cross-validation Mean days between consecutive games, pitches in repertoire, mean pitch speed, horizontal release location