Characteristics of Physiological Changes in Athlete Training Based on the Data Mining Algorithm

Abstract

Objective: In the competition of athletic training, it is imperative to use various physiological and biochemical indicators to study the changes they can bear. Methods: In this paper, national tennis players’ physiological and biochemical indicators are taken as samples, and Artificial Neural Network (ANN) in data mining algorithm is used to classify and predict the sample data. Based on this, to solve the BP neural network's failure in easily falling into a local minimum, the ant colony optimization (ACO) algorithm was introduced to train the changes in the neural network. Finally, the improved BP neural network technology of the ant colony optimization algorithm is used in the model to analyze the physiological changes in tennis players. Results: The research results show that the model successfully predicted the physiological change in athletes and could provide coaches with a basis for decision-making. Conclusions: The physiological change in athletes is combined with the neural network algorithm to establish a connection between the two, which provides an effective and reliable method for detecting the physical function of sports transportation with unique guidance in athletes’ training and competition. Level of evidence II; Therapeutic studies - investigation of treatment results.

Analysis and Research on the Impact of Physical Exercise on Residents’ Health Based on the Improved BP Neural Network Model

“…rough the analysis of the above simulation results, it can be concluded that although the prediction result of BP neural network has a small deviation from the actual value, the analysis of Figure 7 shows that the prediction value of the improved neural network has the same change trend as the actual value, while the change trend of the prediction value of BP neural network is not obvious compared with the actual value, and the deviation of the prediction result is large (Tan and Song [19]). At the same time, Figure 7 shows that the prediction accuracy of the improved neural network is higher than that of BP neural network.…”