Year: 2025
Type: article
Abstract: Avoiding injuries is crucial for athletic performance. Despite the difficulty in predicting injuries, new technologies and data science applications may offer valuable information. The goal was to apply machine learning to predict in juries in runners based on complete training data. Over a seven-year span, 74 elite middle- and long-distance runners were used to test injury prediction. Two methods of analysis were used. Initially, a time series was created that represents the training load for the preceding seven days, with ten features describing the training for each day. These characteristics combined subjective information on training effectiveness and training effort level with data from a watch (such as duration and distance). The average area under the ROC-AUC curve for the day approach were produced to be 0.9978 by a prediction system based on Random Forest Classifier machine learning model. The machine learning-based method predicts a significant percentage of injuries when the model is built using training data recorded on a daily basis in athlete training. All things considered, the results showcase the benefits of applying machine learning to forecast injuries and also customize athlete training regimens.
DOI: 10.14419/hf5g9c06
Institution: University of Mumbai
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7116397374
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