JOURNAL ARTICLE

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

  • Published In: Journal of Intelligent & Fuzzy Systems, 2024, v. 46, n. 4. P. 9999 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chau, Vinh Huy; Vo, Anh Thu; Ngo, Huu Phuc 3 of 3

Abstract

This article focuses on developing a predictive model for the total score of male powerlifters using an improved random forest regression algorithm (RFRA) optimized by the differential squirrel search algorithm (DSSA). The study collected data on age, weight, and total scores from 10,000 powerlifters worldwide and demonstrated that age and weight strongly correlate with performance. The DSSA-RFRA model achieved a prediction error of less than 10%, outperforming standard RFRA and artificial neural network methods, suggesting its potential utility for coaches and athletes in training and competition planning. The study acknowledges limitations due to unconsidered factors such as psychological and health conditions, indicating areas for future research.

Additional Information

  • Source:Journal of Intelligent & Fuzzy Systems. 2024/04, Vol. 46, Issue 4, p9999
  • Document Type:Article
  • Subject Area:Sports and Leisure
  • Publication Date:2024
  • ISSN:1064-1246
  • DOI:10.3233/JIFS-230032
  • Accession Number:176907275
  • Copyright Statement:Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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