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Machine Learning for Running Injury Prediction by Ankitji Thakor is a document available to read on EtoBox.

This study introduces an innovative machine learning approach to analyze biomechanical factors contributing to running-related injuries, utilizing data from 84 active runners. An ensemble model combining Gradient-Boosted Decision Trees, Long Short-Term Memory networks, and Support Vector Machines achieved an accuracy of 88.37%, identifying key predictors such as ground reaction force and stride length. The findings highlight the potential for personalized injury prevention strategies through advanced data a

Author

Ankitji Thakor

Language

EN