## 2 Materials and Methods
### Data Preprocessing  
Data preprocessing is a crucial phase in data analysis and modeling and plays a key role in the quality and effectiveness of the results obtained. In the framework of this research on injuries to athletes, the preprocessing will address various tasks, ensuring that the data are accurate, reliable, and ready for analysis, as part of the preparation for the application of machine learning algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Value** & **Type** \\ \hline Age & A numerical value representing the age of the athlete. & Numeric \\ Gender & Categorical variable indicating the gender of the athlete. & Categorical \\ Hours of training & A numerical value representing the number of training hours. & Numeric \\ Days of training & A numerical value indicating the weekly frequency of training. & Numeric \\ Previous injuries & Categorical variable indicating if the athlete has had previous injuries. & Categorical \\ Corrective treatment & Categorical variable indicating if the athlete has received corrective treatment. & Categorical \\ Sport with injury & Categorical variable indicating if the athlete practices a sport with a risk of injuries. & Categorical \\ Preventive treatment & Categorical variable indicating if the athlete has received preventive treatment. & Categorical \\ Stress & Numerical value related to psychological factors. & Numeric \\ Kinesiophobia & Numerical value related to psychological factors. & Numeric \\ Fatigue & Numerical value related to physical factors. & Numeric \\ Previous warmup & Categorical variable indicating if the athlete performs a warmup before training. & Categorical \\ Average hydration & Numerical value related to average hydration. & Numeric \\ Hydration on event day & Numerical value related to hydration on the day of the event. & Numeric \\ \hline \hline \end{tabular}
\end{table}
Table 2: Predictor variables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value** & **Type** \\ \hline Outcome & Binary variable indicating whether the athlete has experienced an injury. & Binary \\ \hline \hline \end{tabular}
\end{table}
Table 3: Response variable.  
To address the challenge posed by class imbalance in our dataset, specific techniques were implemented during the model training process. Class weighting strategies were employed to assign greater importance to the minority class, and experimentation was conducted with subsampling methods, including the application of the synthetic minority over-sampling technique (SMOTE) [41; 42]. This approach was implemented to alleviate the impact of class imbalance on logistic regression. Furthermore, performance metrics such as precision, recall, and the F1-Score were assessed to comprehensively capture the model's effectiveness in detecting injuries in athletes.  
The initial database comprises 500 surveyed athletes. The database underwent a debugging process to ensure the integrity and consistency of the data. A total of 50 outliers, duplicates, and inconsistent records were identified and addressed. Data cleaning is essential to avoid biases and errors in the subsequent analysis.