（1.Yantai National Physique Monitoring Center，Yantai 264003，China；2.School of Public Health and Management，Binzhou Medical University，Yantai 264003，China；3.School of Clinical Medicine，Binzhou Medical University，Yantai 264003，China；4.Yantai Center for Disease Control and Prevention，Yantai 264003，China)
Abstract: The authors used machine learning models to quickly identify and predict the condition of teenage athletes’ coping ability during the outbreak of COVID-19. The authors utilized the “Questionnaire Star” online questionnaire platform to carry out a questionnaire survey on 1 699 teenage athletes aged 7-17 at 6 sports schools in Yantai, collected their outbreak protection knowledge and behavior information and calculated their coping ability scores, utilize dy-namic clustering analysis to divide coping ability into high and low levels, and established a random forest model to measure the importance of various coping ability affecting factors; by using various affecting factors as input character-istics, the authors established such 3 machine learning models as BP neural network, support vector machine and mul-tivariate adaptive regression spline to classify and predict response levels, and carried out prediction accuracy and clas-sification performance comparison with the Logistic regression model. The results show the followings: the survey par-ticipating teenage athletes’ outbreak protection knowledge knowing degree was not comprehensive enough, nearly 1/2 of the athletes were unable to overcome their nervous and panic psychology, approximately 3/4 of the athletes were unable to complete training plans; outbreak coping ability affecting factor importance ordering results show that age, training program and residence area were listed top 3; machine learning model predication results show that as com-pared with Logistic regression, the average accuracy rate (80.32%) of the support vector machine model based on radial basis function was the highest, 7.15% higher, the sensitivity (0.86) of the multivariate adaptive spline addition model was the highest, 12.24% higher, the specificity (0.83) of the BP neural network model of 5-3-2-1 double hidden layer structure was the highest, 62.11% higher. The results indicate the followings: the machine learning models’ simulation of teenage athletes’ ability to cope with the outbreak of COVID-19 is feasible; the prediction accuracy and classifica-tion performance are better than those of the Logistic regression model; the BPN model with the highest specificity is more capable of judging teenage athletes with a weaker outbreak coping ability, recommended for being used for the quick identification of intervention guidance targets during outbreak control.
Key words: COVID-19；coping ability；machine learning；teenage athlete