## 5 Conclusions  
The objective of this study was to address the issue of using various machine learning methods for injury prediction from the athlete's internal and external loads conjointly. The results of this study show that depending on the complexity of the predictive model, the different predictive metrics values for injury prediction are close to 100%, especially with a 1-month time horizon. In addition, it appears that the subjective variables (i.e., internal load) of the pre-session questionnaire (such as sleep quality, fatigue, shape, mood) as well as post-session questionnaire (satisfaction and pleasure) and RPE are found to be determining factors in the occurrence of injuries. Overall, our findings provide further justification for the implementation of a team-wide monitoring strategy of internal load in elite soccer players. Finally, although the preliminary results of this paper appear encouraging and relevant, future research with a larger sample size by involving several teams from the same championship can provide sufficient data to move from specific conclusions to general ones about machine learning methods. Conceptualization, E.V., N.S.-C. and A.I. ; Formal analysis, E.V. and N.S.-C.; Investigation, E.V. ; Methodology, N.S.-C. and A.I. ; Project administration, J.M. and S.P. ; Supervision, S.P. ; Validation, E.V., N.S.-C., J.M. and S.P. ; Writing--original draft, E.V. ; Writing--review & editing, N.S.-C., A.I. and S.P. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare that there are no conflicts of interest regarding the publication of this paper. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Parameter** & **Accuracy** & **Precision** & **Recall** & **AUC** \\ \hline KNN & K & 1 & 1 & 1 & 4 \\ \hline LDA & tol & 5.12 \(\times 10^{-6}\) & 0 & 0 & 0 \\ \hline \multirow{4}{*}{tree} & criterion & entropy & gini & entropy & entropy \\  & max depth & 50 & 1513 & 10,000 & 9529 \\  & min samples split & 2 & 2 & 2 & 25 \\  & min impurity decrease & 0 & 1.35 \(\times 10^{-4}\) & 0 & 3.5 \(\times 10^{-3}\) \\ \hline gaussianNB & var smoothing & 0.0325 & 0 & 1 \(\times 10^{-4}\) & 0.13 \\ \hline \multirow{4}{*}{forest} & n estimators & 1 \(\times 10^{5}\) & 415 & 252 & 5269 \\  & criterion & entropy & gini & entropy & entropy \\  & max depth & 87 & 29 & 92 & 75 \\  & min samples split & 2 & 2 & 2 & 5 \\  & min impurity decrease & 0 & 0 & 0 & 0 \\  & max features & 0.5 & 0.195 & 0.5 & 0.3638 \\ \hline \multirow{4}{*}{SVM} & C & 176530 & 1 \(\times 10^{7}\) & 1 \(\times 10^{6}\) & 816,071 \\  & gamma & 1.11 & 1 \(\times 10^{-6}\) & 1 \(\times 10^{-6}\) & 1 \(\times 10^{-6}\) \\  & degree & 3 & 8 & 1 & 7 \\ \hline \multirow{4}{*}{MLP} & hidden layer sizes & (50. 100. 100) & (50. 50. 100) & (100. 100. 50) & (100. 100. 100) \\  & activation & relu & relu & relu & relu \\  & alpha & 1 \(\times 10^{-5}\) & 1 \(\times 10^{-5}\) & 1 \(\times 10^{-5}\) & 1 \(\times 10^{-5}\) \\ \hline \multirow{4}{*}{logit} & penalty & l2 & l2 & l2 & l2 \\  & tol 0.98 & 0.01459 & 0.38 & 0.0028 & \\  & C & 3125 & 2976 & 999,758 & 2275 \\  & l1 ratio & 0 & 0 & 0 & 1 \\ \hline \multirow{4}{*}{elasticnet} & penalty & elasticnet & l1 & elasticnet & l1 \\  & tol & 7 \(\times 10^{-4}\) & 1 \(\times 10^{-6}\) & 1 \(\times 10^{-4}\) & 1 \(\times 10^{-6}\) \\  & C & 966341 & 1 \(\times 10^{6}\) & 1 \(\times 10^{6}\) & 683,586 \\  & l1 ratio & 0 & 1 & 0 & 1 \\ \hline \multirow{4}{*}{XGB} & max depth & 5 & 4 & 5 & 10 \\  & n estimators & 737 & 594 & 875 & 904 \\ \cline{1-1}  & eta & 0.256 & 0.45327 & 0.4529 & 0.03162 \\ \cline{1-1}  & gamma & 0 & 0 & 0 & 0 \\ \cline{1-1}  & sampling method & uniform & uniform & gradient based & uniform \\ \hline Ridge & alpha & 33 & 17.1 & 0 & 0 \\ \cline{1-1}  & tol & 1 & 0 & 1 & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Optimal hyper-parameters for 1-month injury prediction. **Figure A1.