A research on the application of college students ' physique Data Mining based on Logistic Regression Algorithm

Authors

  • Tianlong Xiao School of Computer, Bao Ji University of Arts and Sciences, Baoji 721016, China
  • Ping Zhang School of Computer, Bao Ji University of Arts and Sciences, Baoji 721016, China
  • Yuren Zhang School of Computer, Bao Ji University of Arts and Sciences, Baoji 721016, China
  • Dongxu Li School of Computer, Bao Ji University of Arts and Sciences, Baoji 721016, China
  • Jinsong Shen Jiangsu Saideli Diagnostic Technology Co., Ltd, Jingjiang, Jiangsu 214500, China;Jiangsu Saideli Pharmaceutical Machinery Manufacturing Co., Ltd, Jingjiang, Jiangsu 214500, China

DOI

https://doi.org/10.52810/TC.2021.100042

Keywords:

Logistic Regression Algorithm, Data Mining, Physical Health Test, College Students

Abstract

In this paper, we analyze the Logistic Regression Algorithm and give the implementation process of the Logistic Regression Algorithm. Based on the physique test data of 2019 students in Baoji University of Arts and Sciences, we use this algorithm to analyze and extract the classification rules hidden in the physique test data. These classification rules are not only consistent with the original data, but also highly consistent with the physical condition of students. Based on these rules, colleges and universities can quickly determine the physical condition of students, so as to put forward effective, reasonable, and feasible suggestions for physical exercise. Thus, this study plays an important role in the development of college students' physique early warning mechanism and the cultivation of higher education talents.

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References

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A research on the application of college students ' physique Data Mining based on Logistic Regression Algorithm

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Published

2021-09-06

How to Cite

Xiao, T., Zhang, P., Zhang, Y., Li, D., & Shen, J. (2021). A research on the application of college students ’ physique Data Mining based on Logistic Regression Algorithm. ASP Transactions on Computers, 1(2), 12–18. https://doi.org/10.52810/TC.2021.100042

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Section

Regular Paper