An unsupervised machine learning approach to evaluate sports facilities condition in primary school.

ABSTRACT: Sports facilities have been acknowledged as one of the crucial environmental factors for children's physical education, physical fitness, and participation in physical activity. Finding a solution for the effective and objective evaluation of the condition of sports facilities in schools (SSFs) with the responding quantitative magnitude is an uncertain task. This paper describes the utilization of an unsupervised machine learning method to objectively evaluate the condition of sports facilities in primary school (PSSFC). The statistical data of 845 samples with nine PSSFC indicators (indoor and outdoor included) were collected from the Sixth National Sports Facility Census in mainland China (NSFC), an official nationwide quinquennial census. The Fuzzy C-means (FCM) algorithm was applied to

SUBMITTER: Xia J 

PROVIDER: S-EPMC9020747 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature