IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0292557.html
   My bibliography  Save this article

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

Author

Listed:
  • Caixia Wang

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.

Suggested Citation

  • Caixia Wang, 2023. "Optimization of sports effect evaluation technology from random forest algorithm and elastic network algorithm," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0292557
    DOI: 10.1371/journal.pone.0292557
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292557
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292557&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0292557?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    2. Xuecheng He & Jujie Wang, 2024. "A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-27, November.
    3. Murat Tasci & Hidir Duzkaya, 2025. "Estimation of Working Error of Electricity Meter Using Artificial Neural Network (ANN)," Energies, MDPI, vol. 18(5), pages 1-16, March.
    4. Dezheng Zhang & Jing Li & Yonghong Xie & Aziguli Wulamu, 2023. "Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classification," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-22, October.
    5. Chin Soon Ku & Jiale Xiong & Yen-Lin Chen & Shing Dhee Cheah & Hoong Cheng Soong & Lip Yee Por, 2023. "Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market," Mathematics, MDPI, vol. 11(11), pages 1-20, May.
    6. Qin, Fuli & Tong, Mingyu & Huang, Ying & Zhang, Yubo, 2024. "Modeling, prediction and analysis of natural gas consumption in China using a novel dynamic nonlinear multivariable grey delay model," Energy, Elsevier, vol. 305(C).
    7. Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.
    8. Jiahao Chen & Xiaofei Li & Junjie Du, 2025. "Analysis of Frequent Trading Effects of Various Machine Learning Models," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1707-1740, March.
    9. Jin, Ting & Liang, Feiyan & Dong, Xiaoqi & Cao, Xiaojuan, 2023. "Research on land resource management integrated with support vector machine —Based on the perspective of green innovation," Resources Policy, Elsevier, vol. 86(PB).
    10. Thiago Conte & Roberto Oliveira, 2024. "Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazi," Energies, MDPI, vol. 17(4), pages 1-31, February.
    11. Mokhtar Jlidi & Oscar Barambones & Faiçal Hamidi & Mohamed Aoun, 2024. "ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC," Energies, MDPI, vol. 17(12), pages 1-21, June.
    12. Farwah Ali Syed & Kwo-Ting Fang & Adiqa Kausar Kiani & Muhammad Shoaib & Muhammad Asif Zahoor Raja, 2025. "Design of Neuro-Stochastic Bayesian Networks for Nonlinear Chaotic Differential Systems in Financial Mathematics," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 241-270, January.
    13. Pulikandala Nithish Kumar & Nneka Umeorah & Alex Alochukwu, 2024. "Dynamic graph neural networks for enhanced volatility prediction in financial markets," Papers 2410.16858, arXiv.org.
    14. Agnieszka Wawrzyniak & Andrzej Przybylak & Piotr Boniecki & Agnieszka Sujak & Maciej Zaborowicz, 2023. "Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland," Agriculture, MDPI, vol. 13(7), pages 1-13, July.
    15. Peng, Yaohao & de Moraes Souza, João Gabriel, 2024. "Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    16. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    17. Saeed Alqadhi & Hoang Thi Hang & Javed Mallick & Abdullah Faiz Saeed Al Asmari, 2024. "Evaluating landslide susceptibility and landscape changes due to road expansion using optimized machine learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11713-11741, October.
    18. Boyuan Wu & Jia Luo, 2025. "A Novel Improved Binary Optimization Algorithm and Its Application in FS Problems," Mathematics, MDPI, vol. 13(4), pages 1-39, February.
    19. You-Shyang Chen & Jieh-Ren Chang & Ying-Hsun Hung & Jia-Hsien Lai, 2023. "Oversampling Application of Identifying 3D Selective Laser Sintering Yield by Hybrid Mathematical Classification Models," Mathematics, MDPI, vol. 11(14), pages 1-30, July.
    20. Clemens Tegetmeier & Arne Johannssen & Nataliya Chukhrova, 2024. "Artificial Intelligence Algorithms for Collaborative Book Recommender Systems," Annals of Data Science, Springer, vol. 11(5), pages 1705-1739, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0292557. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.