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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0292557 |
| _version_ | 1827773761479245824 |
|---|---|
| author | Caixia Wang |
| author_facet | Caixia Wang |
| author_sort | Caixia Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T13:28:26Z |
| format | Article |
| id | doaj.art-591a017f5c1849c7bbb9836f5e4a4a6e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-11T13:28:26Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-591a017f5c1849c7bbb9836f5e4a4a6e2023-11-03T05:32:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-011810e029255710.1371/journal.pone.0292557Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm.Caixia WangThis 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.https://doi.org/10.1371/journal.pone.0292557 |
| spellingShingle | Caixia Wang Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0292557 |
| work_keys_str_mv | AT caixiawang optimizationofsportseffectevaluationtechnologyfromrandomforestalgorithmandelasticnetworkalgorithm |