Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques
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 9...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/2/570 |
| _version_ | 1797340188593094656 |
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| author | Rocío Elizabeth Duarte Ayala David Pérez Granados Carlos Alberto González Gutiérrez Mauricio Alberto Ortega Ruíz Natalia Rojas Espinosa Emanuel Canto Heredia |
| author_facet | Rocío Elizabeth Duarte Ayala David Pérez Granados Carlos Alberto González Gutiérrez Mauricio Alberto Ortega Ruíz Natalia Rojas Espinosa Emanuel Canto Heredia |
| author_sort | Rocío Elizabeth Duarte Ayala |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T09:59:25Z |
| format | Article |
| id | doaj.art-fd23065437864bebbcb71c979e105242 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:59:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fd23065437864bebbcb71c979e1052422024-01-29T13:42:49ZengMDPI AGApplied Sciences2076-34172024-01-0114257010.3390/app14020570Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning TechniquesRocío Elizabeth Duarte Ayala0David Pérez Granados1Carlos Alberto González Gutiérrez2Mauricio Alberto Ortega Ruíz3Natalia Rojas Espinosa4Emanuel Canto Heredia5School of Health Sciences, Campus Lomas Verdes, Universidad del Valle de México, Lomas Verdes 53220, MexicoDepartment of Engineering, CIIDETEC—Coyoacán, Universidad del Valle de México, Coyoacán 04910, MexicoDepartment of Engineering, CIIDETEC—Querétaro, Universidad del Valle de México, Querétaro 76230, MexicoDepartment of Engineering, CIIDETEC—Coyoacán, Universidad del Valle de México, Coyoacán 04910, MexicoSchool of Health Sciences, Campus Coyoacán, Universidad del Valle de México, Coyoacán 04910, MexicoSchool of Health Sciences, Campus Chihuahua, Universidad del Valle de México, Chihuahua 31625, MexicoThis 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.https://www.mdpi.com/2076-3417/14/2/570injury predictionathlete healthmachine learningsports risk factorspreventive strategieskinesiophobia |
| spellingShingle | Rocío Elizabeth Duarte Ayala David Pérez Granados Carlos Alberto González Gutiérrez Mauricio Alberto Ortega Ruíz Natalia Rojas Espinosa Emanuel Canto Heredia Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques Applied Sciences injury prediction athlete health machine learning sports risk factors preventive strategies kinesiophobia |
| title | Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques |
| title_full | Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques |
| title_fullStr | Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques |
| title_full_unstemmed | Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques |
| title_short | Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques |
| title_sort | novel study for the early identification of injury risks in athletes using machine learning techniques |
| topic | injury prediction athlete health machine learning sports risk factors preventive strategies kinesiophobia |
| url | https://www.mdpi.com/2076-3417/14/2/570 |
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