ArticlePloS one2023
Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Predicting badminton outcomes through machine learning and technical action frequencies.Scientific reports · 2025Article
- Random forest-based identification and ranking of predictive factors for physical activity in Chinese college students.Frontiers in public health · 2025Article
- Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics.Sensors (Basel, Switzerland) · 2024Article
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Abstract
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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