Authors : Mohamed Ahmed Kamel; Rasha Ragheb Atallah

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/bdwdr6vd

Scribd : https://tinyurl.com/yc4kepat

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP239

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Concerns about predicting sports-related injuries, like those experienced while practicing soccer or running, have grown recently due to the use of machine learning techniques for this purpose. The proposed injury prediction framework is based on SVM and an artificial neural network. The proposed model's architecture aids in the runners' injury prediction. The model's creators gathered the datasets of 24 runners. The model was implemented in MATLAB and evaluated with the help of gathered data. Lastly, a comparative analysis has been done between the model and previous attempts. Furthermore, the gathered dataset was used to assess the model's correctness. By now, the accuracy was 93.7%. The writers plan to provide more information in the future, such as the runners' ages and gender.

Keywords : Machine Learning, SVM , Injury, Runner.

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