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Exploration of students' fitness and health management using data mining technology

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  • Jianxun Mao

    (Liaoning Institute of Science and Engineering)

Abstract

Today, college students' fitness and health have become a major social concern, and the scientific management and planning of college students' fitness and health have become particularly important. The aim is to study the application of Internet of Things (IoT) technology, particularly, data mining (DM) in college students' fitness and health management. First, the current situation is explored for the DM technology in China. Then, the matrix-based Apriori algorithm and the C4.5 decision tree algorithm in the DM field are introduced for association rules mining and classification analysis of college students’ health data, respectively. Afterward, some 2018 college graduates are recruited, and their health status is studied using the combination of the matrix-based Apriori algorithm and the C4.5 decision tree algorithm. The results show that the specific associations of the respondents’ seven health dimensions are mined using the matrix-based Apriori algorithm, then the classification rules of health problems are obtained through the C4.5 decision tree algorithm, and respondents’ health problems are classified. Finally, a fitness and health management system based on matrix-based Apriori and C4.5 decision tree algorithms is established. The results provide a practical reference for schools to master students' health. Thus, the application of IoT technology in college students' fitness and health management can help schools and teachers master students’ health status and prevent college students' health problems scientifically.

Suggested Citation

  • Jianxun Mao, 2022. "Exploration of students' fitness and health management using data mining technology," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1008-1018, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01189-6
    DOI: 10.1007/s13198-021-01189-6
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    1. R. Geetha Ramani & Febronica Faustina & Shalika Siddique & K. Sivaselvi, 2021. "Automatic brain tumour detection using image processing and data mining techniques," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 20(1/2), pages 49-65.
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