Department of Primary Education, Jiaozuo Normal College, Jiaozuo, Henan, China.
PLoS One. 2023 Oct 20;18(10):e0292557. doi: 10.1371/journal.pone.0292557. eCollection 2023.
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.
本研究利用先进的数据挖掘和机器学习技术，深入探讨体育活动对身体健康的影响，并为明智选择运动和促进健康提供科学依据。本研究以弹性网络算法为指导，精心构建了一个运动表现评估模型。与传统的最小绝对值收缩和选择算子（Lasso）算法不同，该模型旨在阐明因体育活动而影响身体健康指标的因素。此外，引入随机森林算法有助于全面评估不同维度的运动表现：摔跤类运动、足球类运动、技能类运动和学校体育教育。本研究采用 Top-K 标准进行评估，并与高性能支持向量机（SVM）算法进行对比，根据三个不同的标准（Top-3、Top-5 和 Top-10）来检查精度。本研究的关键创新在于将弹性网络和随机森林算法相结合，从而可以全面考虑不同体育活动对身体健康指标的影响因素。通过这种综合方法，本研究更精确地评估了体育活动的影响，揭示了各种体育活动对身体健康的不同影响。因此，建立了一种更精确的体育表现检测和健康发展评估工具。本研究利用弹性网络算法，在关键的特征选择阶段优化模型构建，有效捕捉与不同体育活动相关的关键影响因素。同时，随机森林算法的集成增强了模型的预测能力，使运动表现评估模型能够全面揭示各种体育活动的影响程度。本研究是运动表现评估领域的一项重要贡献，为运动健康和研究方法提供了有价值的见解和进展。
