A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes (2022)
Jauhiainen, S., Kauppi, J.-P., Krosshaug, T., Bahr, R., Bartsch, J., & Äyrämö, S. (2022). Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes. American Journal of Sports Medicine, 50(11), 2917-2924. https://doi.org/10.1177/03635465221112095
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Jauhiainen, Susanne; Kauppi, Jukka-Pekka; Krosshaug, Tron; Bahr, Roald; Bartsch, Julia; Äyrämö, Sami
Lehti tai sarja: American Journal of Sports Medicine
ISSN: 0363-5465
eISSN: 1552-3365
Julkaisuvuosi: 2022
Ilmestymispäivä: 19.08.2022
Volyymi: 50
Lehden numero: 11
Artikkelin sivunumerot: 2917-2924
Kustantaja: SAGE Publications
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1177/03635465221112095
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/82784
Tiivistelmä
Purpose: To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance.
Study Design: Case-control study; Level of evidence, 3.
Methods: The authors used 3-dimensional motion analysis and physical data collected from 791 female elite handball and soccer players. Four common classifiers were used to predict ACL injuries (n = 60). Area under the receiver operating characteristic curve (AUC-ROC) averaged across 100 cross-validation runs (mean AUC-ROC) was used as a performance metric. Results were confirmed with repeated permutation tests (paired Wilcoxon signed-rank-test; P \ .05). Additionally, the effect of the most common class imbalance handling techniques was evaluated.
Results: For the best classifier (linear support vector machine), the mean AUC-ROC was 0.63. Regardless of the classifier, the results were significantly better than chance, confirming the predictive ability of the data and methods used. AUC-ROC values varied substantially across repetitions and methods (0.51-0.69). Class imbalance handling did not improve the results.
Conclusion: The authors’ approach and data showed statistically significant predictive ability, indicating that there exists information in this prospective data set that may be valuable for understanding injury causation. However, the predictive ability remained low from the perspective of clinical assessment, suggesting that included variables cannot be used for ACL prediction in practice.
YSO-asiasanat: urheilu; urheilijat; joukkueurheilu; loukkaantuminen (fyysinen); urheiluvammat; suorituskyky; ennustettavuus; koneoppiminen; liikeanalyysi
Vapaat asiasanat: predictive methods; machine learning; prediction significance; cross-validation; motion analysis; ACL injury; team sports
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2022
JUFO-taso: 2
