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This study investigates the multifaceted phenomenon of kinesiophobia, characterized by an irrational fear of movement, particularly prevalent among individuals with chronic pain. The research delves into various factors influencing kinesiophobia, including demographic variables, psychological experiences, and physical characteristics. Employing the Tampa Scale of Kinesiophobia, the study assesses fear levels and their correlation with pain perception and functional capacity. Findings reveal a significant relationship between kinesiophobia and psychological exhaustion, reduced alertness, and self-esteem issues, particularly among athletes. Moreover, factors such as fatigue, overextension, sleep quality, and nutritional habits are identified as contributing to the manifestation of kinesiophobia. Furthermore, our study employs meticulous data preprocessing and neural network modeling, achieving an accuracy of 94.54%, to explore predictive factors such as age, gender, and fear-related attitudes. Notably, while injury history emerges as a significant predictor of kinesiophobia, gender does not exert a substantial influence. Notably, while injury history emerges as a significant predictor of kinesiophobia, gender does not exert a substantial influence. The analysis underscores the complex interplay of physical, psychological, and environmental factors in shaping kinesiophobia and highlights the importance of tailored interventions targeting fear-related attitudes and promoting well-being and rehabilitation strategies in individuals affected by this condition.

This innovative study addresses the prevalent issue of sports injuries, particularly focusing on ankle injuries, utilizing advanced analytical tools such as artificial intelligence (AI) and machine learning (ML). Employing a logistic regression model, the research achieves a remarkable accuracy of 90.0%, providing a robust predictive tool for identifying and classifying athletes with injuries. The comprehensive evaluation of performance metrics, including recall, precision, and F1-Score, emphasizes the model’s reliability. Key determinants like practicing sports with injury risk and kinesiophobia reveal significant associations, offering vital insights for early risk detection and personalized preventive strategies. The study’s contribution extends beyond predictive modeling, incorporating a predictive factors analysis that sheds light on the nuanced relationships between various predictors and the occurrence of injuries. In essence, this research not only advances our understanding of sports injuries but also presents a potent tool with practical implications for injury prevention in athletes, bridging the gap between data-driven insights and actionable strategies.