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Integrated framework to integrate Spark-based big data analytics and for health monitoring and recommendation in sports using XGBoost algorithm

  • Application of soft computing
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Abstract

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people’s attention to their health, creating a demand for better medical services. However, China’s ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the XGBoost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the XGBoost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, XGBoost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the XGBoost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naïve Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores the transformative potential of the integrated framework in revolutionizing sports medical services.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Funding

This study was funded by the Ministry of Education Industry Education Collaborative Education Project (Research on the Practice Path of College Students' Innovation and Entrepreneurship Education from the Perspective of Project Achievement Transformation; Project No. 202002201011).

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Authors and Affiliations

  1. Studies Section, School of Physical Education, Southwest Medical University, Luzhou, 646000, Sichuan, China

    Yin Zhao

  2. Graduate School, Jose Rizal University Jose Rizal University, 1552, Mandaluyong, Philippines

    Ma. Finipina Ramos

  3. School of Physical Education, Southwest Medical University, Luzhou, 646000, Sichuan, China

    Bin Li

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  1. Yin Zhao
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Correspondence to Bin Li.

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Zhao, Y., Ramos, M.F. & Li, B. Integrated framework to integrate Spark-based big data analytics and for health monitoring and recommendation in sports using XGBoost algorithm. Soft Comput 28, 1585–1608 (2024). https://doi.org/10.1007/s00500-023-09450-9

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