Explainable artificial intelligence for fitness prediction of young athletes living in unfavorable environmental conditions

Abstract

This study is the first attempt to use explainable machine learning (ML) to investigate the factors influencing physical development and the athlete's performance in the Karakalpakstan and Khwarazm regions of the Republic of Uzbekistan. Three physical fitness tests (Dribbling Shuttle, Goal Accuracy, and Yo-Yo intermittent), anthropometrics, and hematology data (33 subject-specific factors) of 60 young soccer players were used in this study. Results reveal that the explainable ML algorithms were effective in prediction with high model accuracy measured from mean absolute error and mean squared error (0.004 and 0.052 for Dribbling Shuttle test with Random Forest model, 41.783 and 5 for Yo-Yo Intermittent Recovery Test-level with XGB model and 0.027 and 0.135 for Goal Accuracy test with XGB model). In contrast, model bias was estimated using mean absolute percentage error (3.6 % for the Dribbling Shuttle test with the Random Forest model, 0.3 % for the Yo-Yo Intermittent Recovery Test with the XGB model and 36.7 % for the Goal Accuracy test with the XGB model). Furthermore, Shapley additive explanations analysis exhibited that the anthropometric parameters such as body mass, mesomorph, and ankle diameter are dominant in physical development. These results confirm the generally accepted concept of the dominant anthropometric profile of early maturing professional soccer players. Additionally, it was understood that the hematology parameters of red blood count and mean corpuscular volume significantly influence physical development. Overall, explainable AI-based methods are more effective in predicting young athletes' physical development and performance. Therefore, the findings can effectively be incorporated with fitness assessment interventions and enhance related regional policy development.