Analysis and prediction of athlete's anxiety state based on artificial intelligence.

ABSTRACT: Obtaining athletes' anxiety accurately and regulating their psychological state helps improve their competitive performance. Therefore, this article uses a hierarchical clustering algorithm to identify the sources of stress of track and field athletes. A novel and efficient hierarchical clustering algorithm is proposed in this article. The algorithm consists of two stages: dividing and agglomerating. In the dividing stage, the initial data set is taken as a class and subclasses more than the actual number of clusters are obtained through multiple dividing. In the agglomerating phase, the subclasses divided during the dividing process are merged into the correct class. In addition, we construct an analysis model of athletes' anxiety state based on the radial basis function (RBF) model, wher

SUBMITTER: Guo L 

PROVIDER: S-EPMC10280679 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature