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

Author:

Chau Vinh Huy1,Vo Anh Thu1,Ngo Huu Phuc1

Affiliation:

1. Ho Chi Minh City University of Physical Education and Sport, Ho Chi Minh City, Vietnam

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.

Publisher

IOS Press

Reference22 articles.

1. Physical activity is a medicine for non-communicable diseases: a survey study regarding the perception of physical activity impact on health wellbeing;Saqib;Risk Management and Healthcare Policy,2020

2. The relevance of a physical active lifestyle and physical fitness on immune defense: mitigating disease burden, with focus on COVID-19 consequences,;Filgueira;Frontiers in Immunology,2021

3. The roles of physical activity, exercise, and fitness in promoting resilience during adolescence: effects on mental well-being and brain development;Belcher;Biological Psychiatry: Cognitive Neuroscience and Neuroimaging,2021

4. Holtforth, Australian and EASA based pilots’ duty schedules, stress, sleep difficulties, fatigue, wellbeing, symptoms of depression and anxiety,;Venus;Transportation research interdisciplinary perspectives,2022

5. Analysis of USA powerlifting federation data from January 1, –June 11;Ball;The Journal of Strength & Conditioning Research,2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3