A Machine Learning Approach for Predicting Musculoskeletal Injuries Using XGBoost Enhancing Clinical Decision-Making and Preventive Rehabilitation
D. Chandra Mouli Krishna
, Naga Sravana Kumar Jampa , K. Rameshwar
Musculoskeletal Injuries, XGBoost, Machine Learning, Injury Prediction, Physiotherapy, Sports Analytics, Preventive Rehabilitation.
Musculoskeletal injuries, including strains and sprains, represent a substantial burden on healthcare systems and athletic performance worldwide. Conventional injury prediction models are largely dependent on subjective clinical assessments and limited risk factor analysis, restricting their predictive accuracy and scalability. This study presents a supervised machine learning approach using the Extreme Gradient Boosting (XGBoost) algorithm to predict injury risk based on structured datasets incorporating clinical and contextual variables.
"A Machine Learning Approach for Predicting Musculoskeletal Injuries Using XGBoost Enhancing Clinical Decision-Making and Preventive Rehabilitation", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.4, Issue 4, page no.a474-a477, April-2026, Available :https://rjpn.org/JETNR/papers/JETNR2604057.pdf
Volume 4
Issue 4,
April-2026
Pages : a474-a477
Paper Reg. ID: JETNR_233620
Published Paper Id: JETNR2604057
Downloads: 00048
Research Area: Humanities All
Country: -, -, India
ISSN: 2984-9276 | IMPACT FACTOR: 9.87 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.87 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
