# Prediction of Injuries in CrossFit Training: A Machine Learning Perspective
## 3 Results
### Prediction Performance  
Figure 2 shows the confusion matrixes obtained from the six ML models. The following remarks could be extracted: (i) LR and SVM failed to cope with the class imbalance problem leading to very low accuracies for the minority class of injured subjects (54.9% and 53%, respectively). Overall, they both achieved a 10 FCV performance of approximately 72%. (ii) DT and KNN accomplished a similar overall performance (~72%), however they had a better distribution of accuracies among the two classes. Especially, KNN had an almost equivalent performance for participants in both classes (69.7% for class C1 and 73.9% for class C0). (iii) Adaboost accomplished the overall highest 10 FCV performance (74.7%) with 66.6% and 79.6% accuracies for classes C1 and C0, respectively. (iv) RF was the second-best classifier with a 73.22% accuracy and a good trade-off between the accuracies in classes C0 and C1 (79.8% and 62.3%).  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline \multirow{2}{*}{**\#**} & \multirow{2}{*}{**Selected Risk Factor**} & \multicolumn{3}{c}{**N (\%)**} \\  & & **All** & **C0** & **C1** \\ \hline F1 & Gender & & & \\ \hline  & female & 443 (36) & 300 (24.5) & 143 (11.7) \\  & male & 781 (64) & 390 (31.9) & 391 (31.9) \\ \hline F2 & How long have you been participating in CrossFit? & & & \\ \hline  & 0–6 mo & 126 (10.3) & 89 (7.3) & 37 (3) \\  & 6–12 mo & 143 (11.7) & 113 (9.2) & 30 (2.5) \\  & 12–24 mo & 361 (29.5) & 254 (21) & 107 (8.5) \\  & \(\geq\)24 mo & 594 (48.5) & 234 (19.1) & 360 (29.4) \\ \hline F3 & On average, how many days a week do you train in CrossFit? & & & \\ \hline  & 1–2 times/wk & 69 (6) & 45 (4) & 24 (2) \\  & 3–4 times/wk & 592 (48) & 357 (29.1) & 235 (18.9) \\  & \(>\)4 times/wk & 563 (46) & 288 (23.5) & 275 (22.5) \\ \hline F4 & On average, what is the duration of your workout (including warm-up)? & & & \\ \hline  & \textless{1 h} & 98 (8) & 55 (4.5) & 43 (3.5) \\  & \(\geq\)1 h & 1126 (92) & 635 (51.9) & 491 (40.1) \\ \hline F5 & What was the level of your athletic activity–fitness in the last 1 year before you started CrossFit? & & \\ \hline  & Low & 216 (17.6) & 116 (9.4) & 100 (8.2) \\  & Medium & 598 (48.9) & 388 (31.7) & 210 (17.2) \\  & High & 410 (33.5) & 186 (15.2) & 224 (18.3) \\ \hline F6 & Did you mention to your CrossFit trainer from the beginning (before you started training with him) a detailed & & \\ \hline  & yes & 1009 (82) & 603 (49) & 406 (33) \\  & no & 215 (18) & 87 (7.3) & 128 (10.7) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of samples (and percentages) in the entire population as well as in the two classes considered in our study (C0: healthy participants and C1: injured ones).  
Figure 4 shows reliability graphs for each of the six competing ML models that compare how well the predicted outputs of the binary classifiers are calibrated. The average predicted probabilities for binned predictions are shown in the X axis, whereas Y axis denotes the proportion of samples whose class is positive per bin. Being a measure of uncertainty, the reliability graphs of Figure 4 demonstrate how closely the predicted class probability reflects its ground truth likelihood. The two ensemble algorithms (Adaboost and RF) were proved to be the most reliable ones achieving the lowest observed mean average errors (MAE of 0.0797 and 0.0692, respectively) from the ideal calibration condition (see Figure 4).  
Figure 3: (a) Receiver-operating characteristic curves, (b) Precision–recall curves for ML models.  
(shown with the dashed black line). LR accomplished a moderate MAE of 0.0857, whereas the rest of models achieved MAEs higher than 0.1.  
Having accomplished the best accuracy (0.7466), the highest AUC (0.7793), the second highest precision (0.7968), fourth sensitivity (0.7956), second reliability performance and  
Figure 4: Reliability diagrams of the competing ML models.  
second highest specificity (0.6658) among the six models, Adaboost was proved to be the most efficient model overall.