Sports Injury Prediction System using Random Forest Classifier

• Published in: ITM web of conferences Vol. 44; p. 3068
• Main Authors: Shringarpure, Akshay, Shetty, Ronak, Surve, Ajinkya, Vidhate, Amarsinh
• Format: Journal Article
• Language: English
• Published: EDP Sciences 2022
• ISSN: 2271-2097; 2271-2097
• DOI: 10.1051/itmconf/20224403068

One of the largest growing industries in the modern-day world is the sporting industry. Currently valued at around 500 billion USD, with a growth scope of exponential potential, its ability to attract investors is incredible. And just like any other investment. It is part andparcel of the investor’s fiscal responsibility to take good care of their assets. The biggest assets in the sporting industry are of course the players, and the greatest threat to said assets is injuries. We take into consideration said factors and deem it important to solve said issues, and understanding the money involved, the industry sides with us too. We seek to solve the said problemby taking into account all previous injury records and datasets of various players and predicting the kind, number, and severity of the injuries in the future. We seek to create a methodology for such prediction, which applies to all and any sports, being one of the only such models of its kind.