{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T15:24:20Z","timestamp":1777562660035,"version":"3.51.4"},"reference-count":68,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2021,11,28]],"date-time":"2021-11-28T00:00:00Z","timestamp":1638057600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training\/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.<\/jats:p>","DOI":"10.2478\/ijcss-2021-0009","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T01:44:28Z","timestamp":1638150268000},"page":"147-163","source":"Crossref","is-referenced-by-count":17,"title":["A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players"],"prefix":"10.2478","volume":"20","author":[{"given":"M.","family":"Mandorino","sequence":"first","affiliation":[{"name":"Department of Movement, Human and Health Sciences , University of Rome \u201cForo Italico\u201d , Rome , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.J.","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"University of Coimbra , Research Center for Sport and Physical Activity, Faculty of Sport Science and Physical Education , Coimbra , Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Cima","sequence":"additional","affiliation":[{"name":"Computer, Control and Management Engineering Department , Sapienza University of Rome , Rome , Italy ."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Tessitore","sequence":"additional","affiliation":[{"name":"Department of Movement, Human and Health Sciences , University of Rome \u201cForo Italico\u201d , Rome , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"key":"2026042811035632056_j_ijcss-2021-0009_ref_001","doi-asserted-by":"crossref","unstructured":"Andrade, R., Wik, E. 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