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Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm

This study leverages advanced data mining and machine learning techniques to delve deeper into the impact of sports activities on physical health and provide a scientific foundation for informed sports selection and health promotion. Guided by the Elastic Net algorithm, a sports performance assessme...

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Autor principal: Wang, Caixia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588863/
https://www.ncbi.nlm.nih.gov/pubmed/37862380
http://dx.doi.org/10.1371/journal.pone.0292557
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author Wang, Caixia
author_facet Wang, Caixia
author_sort Wang, Caixia
collection PubMed
description This study leverages advanced data mining and machine learning techniques to delve deeper into the impact of sports activities on physical health and provide a scientific foundation for informed sports selection and health promotion. Guided by the Elastic Net algorithm, a sports performance assessment model is meticulously constructed. In contrast to the conventional Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, this model seeks to elucidate the factors influencing physical health indicators due to sports activities. Additionally, the incorporation of the Random Forest algorithm facilitates a comprehensive evaluation of sports performance across distinct dimensions: wrestling-type sports, soccer-type sports, skill-based sports, and school physical education. Employing the Top-K criterion for evaluation and juxtaposing it with the high-performance Support Vector Machine (SVM) algorithm, the accuracy is scrutinized under three distinct criteria: Top-3, Top-5, and Top-10. The pivotal innovation of this study resides in the amalgamation of the Elastic Net and Random Forest algorithms, permitting a holistic contemplation of the influencing factors of diverse sports activities on physical health indicators. Through this integrated methodology, the research achieves a more precise assessment of the effects of sports activities, unveiling a range of impacts various sports have on physical health. Consequently, a more refined assessment tool for sports performance detection and health development is established. Capitalizing on the Elastic Net algorithm, this research optimizes model construction during the pivotal feature selection phase, effectively capturing the crucial influencing factors associated with different sports activities. Concurrently, the integration of the Random Forest algorithm augments the predictive prowess of the model, enabling the sports performance assessment model to comprehensively unveil the extent of impact stemming from various sports activities. This study stands as a noteworthy contribution to the arena of sports performance assessment, offering substantial insights and advancements to both sports health and research methodologies.
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spelling pubmed-105888632023-10-21 Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm Wang, Caixia PLoS One Research Article This study leverages advanced data mining and machine learning techniques to delve deeper into the impact of sports activities on physical health and provide a scientific foundation for informed sports selection and health promotion. Guided by the Elastic Net algorithm, a sports performance assessment model is meticulously constructed. In contrast to the conventional Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, this model seeks to elucidate the factors influencing physical health indicators due to sports activities. Additionally, the incorporation of the Random Forest algorithm facilitates a comprehensive evaluation of sports performance across distinct dimensions: wrestling-type sports, soccer-type sports, skill-based sports, and school physical education. Employing the Top-K criterion for evaluation and juxtaposing it with the high-performance Support Vector Machine (SVM) algorithm, the accuracy is scrutinized under three distinct criteria: Top-3, Top-5, and Top-10. The pivotal innovation of this study resides in the amalgamation of the Elastic Net and Random Forest algorithms, permitting a holistic contemplation of the influencing factors of diverse sports activities on physical health indicators. Through this integrated methodology, the research achieves a more precise assessment of the effects of sports activities, unveiling a range of impacts various sports have on physical health. Consequently, a more refined assessment tool for sports performance detection and health development is established. Capitalizing on the Elastic Net algorithm, this research optimizes model construction during the pivotal feature selection phase, effectively capturing the crucial influencing factors associated with different sports activities. Concurrently, the integration of the Random Forest algorithm augments the predictive prowess of the model, enabling the sports performance assessment model to comprehensively unveil the extent of impact stemming from various sports activities. This study stands as a noteworthy contribution to the arena of sports performance assessment, offering substantial insights and advancements to both sports health and research methodologies. Public Library of Science 2023-10-20 /pmc/articles/PMC10588863/ /pubmed/37862380 http://dx.doi.org/10.1371/journal.pone.0292557 Text en © 2023 Caixia Wang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Caixia
Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm
title Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm
title_full Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm
title_fullStr Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm
title_full_unstemmed Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm
title_short Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm
title_sort optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588863/
https://www.ncbi.nlm.nih.gov/pubmed/37862380
http://dx.doi.org/10.1371/journal.pone.0292557
work_keys_str_mv AT wangcaixia optimizationofsportseffectevaluationtechnologyfromrandomforestalgorithmandelasticnetworkalgorithm