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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

LĂ³pez-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