AI-Assisted Prediction Models for Primary Anterior Cruciate Ligament Injury Risk and Recovery Time Using Basketball Athlete Performance Data

ACL injuries are among the most prevalent musculoskeletal injuries, particularly affecting basketball athletes. Traditionally, clinicians relied on factors such as demographic characteristics to estimate the risk of injury, which can be limited in predictive accuracy. Recent advances in data analysis and machine learning have opened new opportunities to improve risk prediction by leveraging large datasets. Furthermore, most existing studies rely heavily on systematic reviews and lack advanced machine learning models. Therefore, this research project effectively incorporates data analysis, Python plotting, AI, and machine learning prediction models to assess primary anterior cruciate ligament (ACL) injury risk and recovery time. By analyzing large basketball athlete performance datasets, many risk factors were found to increase ACL injuries and lead to longer recovery times (height, training intensity, age, etc). Among the models tested, Random Forest outperformed other models, and "Rest Days per Week" was one of the most critical predictors for preventing injuries. 

Explain the key factors influencing ACL injury risk and recovery time in basketball athletes, including age, gender, training intensity, and rehabilitation methods.

Apply machine learning models such as Random Forest, Gradient Boosting, and Logistic Regression to analyze athlete data and predict ACL injury risk and recovery duration.

Design targeted injury prevention and rehabilitation strategies for basketball athletes using AI-driven insights and predictive modeling outcomes.

Authors

Ben Zhang is a student researcher with the YRI Foundation whose interests include biology, health, orthopedics, and neuroscience. His project applies artificial intelligence and machine learning to predict ACL injury risk and recovery time by analyzing clinical and biomechanical data. Having experienced a complete ACL rupture requiring surgery and rehabilitation, Ben is motivated to reduce the suffering these injuries cause by advancing predictive tools for prevention and recovery. He has completed research internships, served as a co-associate and research mentor, and has written research papers.

Subham Kumar Jalan holds a Ph.D. in Power Electronics from IIITDM Kancheepuram and an M.Tech in Power Electronics and Drives from MNIT Jaipur. With six years of combined research and industry experience, he has worked as a Control Engineer at Metzuyan Technology Pvt. Ltd., focusing on wind turbine control optimization for GE and SGRE. His expertise includes advanced control, hardware design, MATLAB/Simulink, Python, and data analysis. He has also served as a senior research fellow investigating advanced control strategies for grid-tied solar PV systems and has led workshops on power electronics applications in renewable energy.

Vishal Kumar is a first-generation entrepreneur passionate about building innovative ventures that make a meaningful impact. He thrives on challenges that require creative thinking and strategic execution, balancing vision with action to turn ideas into reality. Embracing resilience, adaptability, and the power of community, Vishal aims to create not just a business, but a legacy that empowers others and drives growth. Committed to learning and sharing experiences, he connects with like-minded individuals to inspire, innovate, and make a difference in every endeavor.