Powerlifting total score prediction based on an improved random forest regression algorithm

Article type: Research Article

Authors: Chau, Vinh Huy^{; *} | Vo, Anh Thu | Ngo, Huu Phuc

Affiliations: Ho Chi Minh City University of Physical Education and Sport, Ho Chi Minh City, Vietnam

Correspondence: [*] Corresponding author. Vinh Huy Chau, Ho Chi Minh City University of Physical Education and Sport, Ho Chi Minh City, 700000, Vietnam. E-mail:huyvcc@upes.edu.vn.

Abstract: This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition.

Keywords: Artificial intelligence, random forest, powerlifting, total score

DOI: 10.3233/JIFS-230032

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 9999-10004, 2024