An Efficient Approach to Association Rule Mining Using the Apriori Algorithm: A Case Study on Enhancing Sports Performance in University Students
Year: 2024
Type: conference-paper
Abstract: The 2022 edition of the Dietary Guidelines for Chinese Locals indicates that the occurrence of overweight and excessive weight among Chinese university students gets on the rise, while their sporting activities efficiency is decreasing. This write-up offers research study focused on finding possible remedies to boost university students' sports performance. An association policy mining method based on the Apriori algorithm was made use of to discover the relationship between different workout programs and the failing indicators observed in fitness tests. The results revealed that HIIT performed at 90%-97% of HRmax worked in reducing body fat percentage amongst pupils with an overweight BMI and was specifically beneficial for boosting explosive rate and muscular cardio endurance. On the other hand, HIIT conducted at 80%-89% HRmax was a lot more efficient in improving cardiorespiratory feature and muscular stamina. Furthermore, MICT at 75%-79% HRmax can effectively decrease body fat percentage and enhance adaptability in students with overweight BMI. This paper highlights the strong link between computer technology and the advancement of tailored exercise plans, demonstrating just how computer system formulas can extra precisely enhance the sports performance of university students.
Authors: Wanting Lv, Shan Li
Institution: Xi'an Physical Education University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410237307