Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques

Author:

Ayala Rocío Elizabeth Duarte1,Granados David Pérez2ORCID,Gutiérrez Carlos Alberto González3,Ruíz Mauricio Alberto Ortega24,Espinosa Natalia Rojas5ORCID,Heredia Emanuel Canto6

Affiliation:

1. School of Health Sciences, Campus Lomas Verdes, Universidad del Valle de México, Lomas Verdes 53220, Mexico

2. Department of Engineering, CIIDETEC—Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico

3. Department of Engineering, CIIDETEC—Querétaro, Universidad del Valle de México, Querétaro 76230, Mexico

4. School of Science and Technology, University of London, London EC1V 0HB, UK

5. School of Health Sciences, Campus Coyoacán, Universidad del Valle de México, Coyoacán 04910, Mexico

6. School of Health Sciences, Campus Chihuahua, Universidad del Valle de México, Chihuahua 31625, Mexico

Abstract

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.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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