Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players

Area of Science:

• Sports Medicine
• Biomechanics
• Data Science

Background:

• Knee joint injuries are prevalent in football, necessitating effective prevention strategies.
• Current prediction methods rely on expert interviews and historical data, often yielding suboptimal accuracy.
• Identifying key injury factors is essential for improving predictive models.

Purpose of the Study:

• To develop an intelligent algorithm for predicting potential knee joint injuries in football players.
• To enhance the accuracy and efficiency of knee injury prediction compared to existing methods.
• To analyze crucial knee injury factors specific to football players.

Main Methods:

• Gathering and analyzing characteristics of knee joint injuries and football player-specific injury factors.
• Developing a novel prediction algorithm based on similarity measurement.
• Validating the algorithm using healthcare data and experimental results.

Main Results:

• The proposed intelligent algorithm demonstrates higher prediction accuracy for knee injuries.
• The algorithm achieves a shorter prediction time compared to traditional methods.
• Key factors contributing to football knee injuries were identified through data analysis.

Conclusions:

• The developed intelligent algorithm offers a more accurate and efficient approach to predicting football player knee injuries.
• Understanding specific injury factors significantly improves prediction capabilities.
• This algorithm can aid in proactively mitigating knee joint harm in athletes.