## 3 Results of Data Training and Discussion
### Neural Network Modeling  
A comprehensive assessment of the models was carried out, and the accuracy achieved by each model is presented in Table 3.  
The features included age, gender (male and female), injury, deceleration injury, overuse injury, training hours, training days, overexertion, previous injuries, corrective  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & **Value** & **Type** \\ \hline Preventive treatment & A categorical variable indicating if the athlete has received & Categorical \\  & preventive treatment & Categorical \\ Sports with injuries & A categorical variable indicating if the athlete continues to practice sports & Categorical \\  & despite injuries & Categorical \\  & A categorical variable indicating if the athlete does not follow medical & Categorical \\  & recommendations & Categorical \\ Stress injury relationship & A categorical variable indicating if stress is related to the injury & Categorical \\  & Self-esteem injury relationship & A categorical variable indicating if self-esteem is related to the injury & Categorical \\  & Fear of injury & Categorical \\  & Fear of pain & Categorical \\  & Fear of increasing pain & Categorical \\  & Fear of continuing sports with pain & A categorical variable indicating fear of continuing sports despite pain & Categorical \\  & Fear of continuing training & A categorical variable indicating fear of continuing training despite pain & Categorical \\  & Healthy nutrition & A categorical variable indicating if the athlete follows a healthy diet & Categorical \\  & A categorical variable indicating if the athlete receives & Categorical \\  & nutritional supervision & Categorical \\  & & Categorical \\  & Warm-up & A categorical variable indicating if the athlete performs a warm-up & Categorical \\  & before training & Categorical \\  & Restful sleep & A categorical variable indicating if the athlete enjoys restful sleep & Categorical \\  & A categorical variable indicating if the athlete consumes & Categorical \\  & isotonic beverages & Categorical \\  & A categorical variable indicating if the athlete consumes & Categorical \\  & alcoholic beverages & Categorical \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Value** & **Type** \\ \hline Outcome & A binary variable that indicates whether & \\  & the athlete has experienced kinesiophobia & Binary \\ \hline \hline \end{tabular}
\end{table}
Table 2: Response variable.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy** \\ \hline Neural network & 94.5\% \\ Linear SVM & 85.3\% \\ SVM Kernel & 88.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of various machine learning algorithms.  
treatment, preventive treatment, sports with injuries, non-compliance, stress injury relationship, self-esteem injury relationship, fear of injury, fear of pain, fear of increasing pain, fear of continuing sports with pain, fear of continuing training, healthy nutrition, nutritional supervision, warm-up, restful sleep, isotonic beverages, alcoholic beverages, and outcome.  
Notably, the neural network model demonstrated exceptional accuracy, achieving an impressive 87.9% during testing. This indicates an outstanding ability to accurately classify athletes based on the presence or absence of kinesiophobia. Interestingly, the boosted trees and logistic regression models also exhibited high levels of accuracy, with values of 87.1% and 85.8%, respectively. These findings have significant implications for the identification and prevention of kinesiophobia in athletes.