Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm

Area of Science:

• Sports Science
• Data Mining
• Injury Prevention

Background:

• Basketball is a popular sport among college students, with increasing participation.
• The sport's accessibility and growing popularity necessitate proactive injury prevention strategies.
• Previous research has not fully explored data-driven approaches for early basketball injury risk assessment.

Purpose of the Study:

• To apply an attribute reduction algorithm for early warning of basketball injury risk.
• To identify key attributes contributing to basketball-related injuries in college athletes.
• To develop a method for preventing severe injuries through data analysis.

Main Methods:

• Utilized an attribute reduction algorithm, a core concept in knowledge discovery.
• Employed attribute frequency as heuristic information for attribute selection.
• Focused on solving attribute selection problems, particularly when attribute frequencies are identical.

Main Results:

• The study successfully applied the attribute reduction algorithm to analyze basketball injury risks.
• Identified critical factors contributing to potential injuries through data reduction.
• Demonstrated the algorithm's effectiveness in addressing attribute selection challenges.

Conclusions:

• Attribute reduction algorithms can be effectively used for early warning systems in sports injuries.
• Objective analysis of sports training data is crucial for identifying and mitigating injury causes.
• This approach offers a valuable tool for enhancing basketball player safety and performance.