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Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury
- Source :
- Sports biomechanics. 22(1)
- Publication Year :
- 2021
-
Abstract
- Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different sample, while a secondary aim was to examine its ability to predict future ACL injuries. The examined outcome measure was 'healthy-limb' class membership probability, which was compared between a cohort of athletes without previous or future (No Injury) previous (PACL) and future ACL injury (FACL). The No Injury group had significantly higher probabilities than the PACL (p < 0.001; medium effect) and FACL group (p ≤ 0.045; small effect). The ability to predict group membership was poor for the PACL (area under curve [AUC]; 0.61
- Subjects :
- Anterior cruciate ligament
medicine.medical_treatment
Physical Therapy, Sports Therapy and Rehabilitation
Cross-validation
Machine Learning
medicine
Humans
Orthopedics and Sports Medicine
Cumulative incidence
Anterior Cruciate Ligament
Rehabilitation
Group membership
biology
Athletes
business.industry
Anterior Cruciate Ligament Injuries
biology.organism_classification
medicine.disease
ACL injury
Biomechanical Phenomena
medicine.anatomical_structure
Cohort
Athletic Injuries
business
Algorithm
Algorithms
Subjects
Details
- ISSN :
- 17526116
- Volume :
- 22
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Sports biomechanics
- Accession number :
- edsair.doi.dedup.....b612d2e1f46742c8bd818d44af4548d0