Technical comparison.

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.

本研究借助先进的数据挖掘与机器学习技术，深入探究体育运动对身体健康的影响，为科学选择运动项目、推进健康促进提供坚实的理论依据。本研究以弹性网（Elastic Net）算法为指导，精心构建了运动表现评估模型。与传统的最小绝对收缩与选择算子（Least Absolute Shrinkage and Selection Operator, Lasso）算法不同，该模型旨在阐明体育运动对身体健康指标的影响因素。此外，引入随机森林（Random Forest）算法，可对四大维度的运动表现开展全方位评估：摔跤类运动、足球类运动、技能型运动以及学校体育课程。本研究采用Top-K准则开展模型评估，并将其与高性能支持向量机（Support Vector Machine, SVM）算法进行对比，在Top-3、Top-5、Top-10三项准则下对模型精度进行校验。本研究的核心创新点在于融合弹性网与随机森林算法，能够全面考量各类体育运动对身体健康指标的影响因素。通过这一集成方法，本研究实现了对体育运动影响的更为精准的评估，揭示了不同体育运动对身体健康的多维度作用效果。据此，本研究构建了更为精细化的运动表现检测与健康发展评估工具。本研究依托弹性网算法，在关键的特征选择阶段优化模型构建流程，有效捕捉与不同体育运动相关的核心影响因素。同时，随机森林算法的融入进一步提升了模型的预测性能，使运动表现评估模型能够全面揭示各类体育运动的影响程度。本研究为运动表现评估领域作出了重要贡献，可为运动健康领域与相关研究方法学发展提供极具价值的研究见解与技术进展。