A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes (2021)

Jauhiainen, S., Kauppi, J.-P., Leppänen, M., Pasanen, K., Parkkari, J., Vasankari, T., Kannus, P., & Äyrämö, S. (2021). New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes. International Journal of Sports Medicine, 42(02), 175-182. https://doi.org/10.1055/a-1231-5304

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Jauhiainen, Susanne; Kauppi, Jukka-Pekka; Leppänen, Mari; Pasanen, Kati; Parkkari, Jari; Vasankari, Tommi; Kannus, Pekka; Äyrämö, Sami

Lehti tai sarja: International Journal of Sports Medicine

ISSN: 0172-4622

eISSN: 1439-3964

Julkaisuvuosi: 2021

Ilmestymispäivä: 13.09.2020

Volyymi: 42

Lehden numero: 02

Artikkelin sivunumerot: 175-182

Kustantaja: Georg Thieme Verlag KG

Julkaisumaa: Saksa

Julkaisun kieli: englanti

DOI: https://doi.org/10.1055/a-1231-5304

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/88829

Tiivistelmä

The purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for risk factors and confirming the predictive power of previously recognized ones. We used three-dimensional motion analysis and physical data from 314 young basketball and floorball players (48.4% males, 15.72±1.79 yr, 173.34±9.14 cm, 64.65±10.4 kg). Both linear (L1-regularized logistic regression) and non-linear methods (random forest) were used to predict moderate and severe knee and ankle injuries (N=57) during three-year follow-up. Results were confirmed with permutation tests and predictive risk factors detected with Wilcoxon signed-rank-test (p<0.01). Random forest suggested twelve consistent injury predictors and logistic regression twenty. Ten of these were suggested in both models; sex, body mass index, hamstring flexibility, knee joint laxity, medial knee displacement, height, ankle plantar flexion at initial contact, leg press one-repetition max, and knee valgus at initial contact. Cross-validated areas under receiver operating characteristic curve were 0.65 (logistic regression) and 0.63 (random forest). The results highlight the difficulty of predicting future injuries, but also show that even with models having relatively low predictive power, certain predictive injury risk factors can be consistently detected.

YSO-asiasanat: liikuntalääketiede; koneoppiminen; ennusteet; riskitekijät; urheiluvammat; loukkaantuminen (fyysinen); polvet; nilkat; koripallo; salibandy; junioriurheilu

Vapaat asiasanat: sports medicine; predictive methods; machine learning; knee injuries; ankle injuries; basketball and floorball

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2021

JUFO-taso: 1