现代信息科技2024，Vol.8Issue(4)：41-45,5.DOI:10.19850/j.cnki.2096-4706.2024.04.009
基于数据挖掘的校园运动研究
Research on Campus Sports Based on Data Mining
摘要
Abstract
Physical fitness testing,as the fundamental way to provide feedback on the physical health level of college students,provides data support for universities to carry out student health intervention work.However,it has become particularly important to scientifically analyze and reasonably use physical fitness data.This paper uses data mining techniques to study the physical measurement data of college students,and uses decision trees,naive Bayes,and Bayesian neural networks to predict the physical measurement data.The results show that Bayesian neural networks have the highest prediction accuracy.By using the CART decision tree to classify physical testing data,the optimal decision tree can be obtained.It analyzes the important factors that affect the physical fitness level of college students through the optimal decision tree,further explore the impact and role of physical testing scores on the physical fitness of college students,and thereby enhance their enthusiasm and interest in participating in campus sports.关键词
数据挖掘/决策树/朴素贝叶斯/贝叶斯神经网络/校园运动Key words
data mining/Decision Tree/naive Bayes/Bayesian Neural Networks/campus sports分类
信息技术与安全科学引用本文复制引用
周义,陈婕,孟翔,汪小芸,张豹..基于数据挖掘的校园运动研究[J].现代信息科技,2024,8(4):41-45,5.基金项目
贵州省2022年省级大学生创新创业训练计划项目(S202214440127) （S202214440127）
