{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T10:30:27Z","timestamp":1757586627320,"version":"3.40.5"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,6,10]]},"abstract":"<jats:p>As the level of soccer in our country has improved rapidly, the level of skill has gradually improved, and the requirements for training of athletes have increased. Due to changes in athlete training methods, it has been decided that athletes must bear a great risk of sports injuries. Accurate prediction of injuries is very important for the development of youth soccer. Based on this, this paper proposes a text classification algorithm based on machine learning and builds a sports injury prediction model that can accurately predict athlete injuries, reduce athlete injuries during training, and be effective. We put forward various sports suitable for young athletes, and put forward some measures to prevent and alleviate athletes\u2019 injuries. This article selects 48 football players from a college of physical education of a university for testing. The athletes participating in the experiment use professional equipment to collect exercise volume and exercise load data, and real-time records of each athlete's physical fitness data within half a year, through the athlete's exercise volume, exercise load, body metabolism, and physical indicators to predict their sports injury. Experiments show that from the degree of injury, it can be seen that the severe injury is the least, with 5 cases of muscle injury, 2 cases of fascia ligament injury, and 1 case of joint injury. There were 25 cases of mild injuries, accounting for 41.0% of the total. This is because the athlete\u2019s sports injury prediction model has better prediction capabilities, allowing athlete coaches and therapists to optimize training courses, ultimately preventing injuries, improving training levels, and reducing rehabilitation costs.<\/jats:p>","DOI":"10.1155\/2021\/2955215","type":"journal-article","created":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T20:05:07Z","timestamp":1623441907000},"page":"1-10","source":"Crossref","is-referenced-by-count":10,"title":["Prediction Model of Juvenile Football Players\u2019 Sports Injury Based on Text Classification Technology of Machine Learning"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1917-779X","authenticated-orcid":true,"given":"Kai","family":"He","sequence":"first","affiliation":[{"name":"College of Sports Science and Technology of Wuhan Sports University, Wuhan 430079, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3233\/jcm-180836"},{"issue":"2","key":"2","first-page":"178","article-title":"Feature selection method based on statistics of compound words for Arabic text classification","volume":"16","author":"A. 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Carapinheira","year":"2018","journal-title":"Revista Iberoamericana de Psicologia del Ejercicio y el Deporte"},{"issue":"5","key":"17","first-page":"465","article-title":"Screening of different metabolites in teenage football players after exercise fatigue","volume":"36","author":"F. Guo","year":"2020","journal-title":"Zhongguo Ying Yong Sheng Li Xue Za Zhi Zhongguo Yingyong Shenglixue Zazhi Chinese Journal of Applied Physiology"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.29070\/13\/57121"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.5206\/wurjhns.2017-18.30"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1257\/jep.31.2.87"},{"issue":"2","key":"21","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","article-title":"A survey of data mining and machine learning methods for cyber security intrusion detection","volume":"18","author":"A. 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