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

被引:1
|
作者
Zhao, Yin [1 ]
Ramos, Ma. Finipina [2 ]
Li, Bin [3 ]
机构
[1] Southwest Med Univ, Sch Phys Educ, Studies Sect, Luzhou 646000, Sichuan, Peoples R China
[2] Jose Rizal Univ Jose Rizal Univ, Grad Sch, Mandaluyong 1552, Philippines
[3] Southwest Med Univ, Sch Phys Educ, Luzhou 646000, Sichuan, Peoples R China
关键词
Big data; Spark; Data mining; XGBoost algorithm; Sports medical integration; Service system construction;
D O I
10.1007/s00500-023-09450-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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), Naive 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.
引用
收藏
页码:1585 / 1608
页数:24
相关论文
共 43 条
  • [21] Distributed L-diversity using spark-based algorithm for large resource description frameworks data
    Jeon, MinHyuk
    Temuujin, Odsuren
    Ahn, Jinhyun
    Im, Dong-Hyuk
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (07) : 7270 - 7286
  • [22] Implementation of the Demographic-Based Recommendation Algorithm Using Big Data
    Li Wenzhe
    Grigorev, Stanislav V.
    2022 11TH INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND APPLICATIONS, ICICA, 2022, : 1 - 5
  • [23] AN INTELLIGENT MONITORING SYSTEM FOR SPORTS MENTAL HEALTH STATUS BASED ON BIG DATA
    Han, Youliang
    Geng, Wenguang
    SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2025, 26 (01): : 231 - 240
  • [24] An insight into tree based machine learning techniques for big data Analytics using Apache Spark
    Sheshasaayee, Ananthi
    Lakshmi, J. V. N.
    2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, INSTRUMENTATION AND CONTROL TECHNOLOGIES (ICICICT), 2017, : 1740 - 1743
  • [25] An Implementation of Hybrid Enhanced Sentiment Analysis System using Spark ML Pipeline: A Big Data Analytics Framework
    Raviya, K.
    Vennila, Mary S.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (05) : 323 - 329
  • [26] Design of College Students' physical health monitoring APP based on sports health big data
    Zhang, Xiaoni
    Li, Ran
    Li, Yunwei
    Wang, Yunsheng
    Wu, Feilong
    INTERNET TECHNOLOGY LETTERS, 2024, 7 (05)
  • [27] NIDS-VSB: Network Intrusion Detection System for VANET Using Spark-Based Big Data Optimization and Transfer Learning
    Ullah, Farhan
    Srivastava, Gautam
    Ullah, Shamsher
    Yoshigoe, Kenji
    Zhao, Yue
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2024, 70 (01) : 1798 - 1809
  • [28] An Integrated Framework for Health State Monitoring in a Smart Factory Employing IoT and Big Data Techniques
    Yu, Wenjin
    Liu, Yuehua
    Dillon, Tharam
    Rahayu, Wenny
    Mostafa, Fahed
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (03): : 2443 - 2454
  • [29] Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach
    Entezami, Alireza
    Sarmadi, Hassan
    Behkamal, Behshid
    Mariani, Stefano
    SENSORS, 2020, 20 (08)
  • [30] A distributed evolutionary based instance selection algorithm for big data using Apache Spark
    Qin, Liyang
    Wang, Xiaoli
    Yin, Linzi
    Jiang, Zhaohui
    APPLIED SOFT COMPUTING, 2024, 159