Evaluation of Children’s Physical Fitness Index and Prediction of Health Risk Trend Based on BP Neural Network Algorithm

Abstract

On the basis of literature review and expert interview, this study constructs the indexes of health fitness evaluation, obtains the difference of the indexes before and after the 15-week health fitness intervention, and establishes the health risk trend predictive equation based on BP neural network algorithm. The results of the study are as follows: after 15 weeks of health fitness intervention, there were significant differences in body fat rate, waist circumference, and waist-to-hip ratio ( P < 0.01 ). There were significant differences in maximal oxygen uptake, 12-minute running distance, one-minute sit-ups, push-ups, standing long jump, pull-ups, and sitting forward flexion ( P < 0.05 ). Body fat percentage, maximal oxygen uptake, forward bending in sitting position, and standing long jump can be used to evaluate the level of children’s physical fitness. In conclusion, after 15 weeks of health and fitness intervention course, the children’s health and fitness were improved. Health and Physical Fitness Intervention Curriculum can be promoted in schools so that more children can benefit from it. And the health risk trend prediction model based on BP neural network algorithm has a certain validity.