Identifying factors affecting sports achievement and endurance using artificial intelligence

Abstract Introduction Sports achievements and performance are not solely based on outstanding endurance, especially in tactical and technical sports. Artificial intelligence (AI) can help us to identify correlations that can contribute to the successful preparation of athletes. Purpose Our aim was to further analyze and evaluate our previously initiated AI-based sports performance studies, identifying the parameters that determine athletes’ achievement and endurance. Methods First, sports cardiology screening was performed in all athletes containing the following examinations: patient’s history, ECG, laboratory test, body composition analysis, echocardiography and cardiopulmonary exercise testing. A database from the sports cardiology screening results was established. Then, we created two scoring systems based on the best ever result (Achievement Score) and the performance on the cardiopulmonary exercise test (Endurance Score). We identified the most important influencing factors using a neural network and characterized the strength of the variables using Shappley Additive Explanation (SHAP) values. Results We examined 1932 tests of 891 athletes and the AI analysis included 917 tests of 546 athletes (20.2±6.2 years, males: 397, 72.7%). Majority of the examined athletes were swimmers (27.4%), followed by basketball players (21.8%), water polo players (15.2%), handball players (13.3%) and football players (13.1%). The most important Achievement Score determinants were the weekly training hours (SHAP=0.27), training years (SHAP=0.26) and the age (SHAP=0.15). Meanwhile, Endurance Score was mainly affected by skeletal muscle mass (SHAP=0.85), weight (SHAP=0.6) and peak heart rate during exercise (SHAP=0.32). Our results were validated on a test population (N=102) and in the Achievement Score the mean absolute error (MAE) was 0.64, the determination coefficient (R2) was 0.56, in Endurance Score the MAE was 0.84 and the R2 was 0.71. Conclusion Based on our results, while endurance is mainly determined by the athlete’s physical characteristics, in athletic achievement experience is more important. With our scoring systems the athlete’s achievement and endurance can be predicted well.

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Journal
European Heart Journal
Published
2024-10-01
DOI
https://doi.org/10.1093/eurheartj/ehae666.2989
Primary Topic
Diverse Approaches in Healthcare and Education Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Identifying factors affecting sports achievement and endurance using artificial intelligence

Hajnalka Vágó, Nóra Sydó, Dorottya Balla, Zoltán Tősér et al.
European Heart Journal
Diverse Approaches in Healthcare and Education Studies
article

Identifying factors affecting sports achievement and endurance using artificial intelligence

Hajnalka Vágó, Nóra Sydó, Dorottya Balla, Zoltán Tősér, Levente Staub, Attila D Kovács, Tamás Takács, Emese Csulak, András Kiss, И. М. Петров, G Y Bohus, B Merkely
article en

Abstract

Abstract Introduction Sports achievements and performance are not solely based on outstanding endurance, especially in tactical and technical sports. Artificial intelligence (AI) can help us to identify correlations that can contribute to the successful preparation of athletes. Purpose Our aim was to further analyze and evaluate our previously initiated AI-based sports performance studies, identifying the parameters that determine athletes’ achievement and endurance. Methods First, sports cardiology screening was performed in all athletes containing the following examinations: patient’s history, ECG, laboratory test, body composition analysis, echocardiography and cardiopulmonary exercise testing. A database from the sports cardiology screening results was established. Then, we created two scoring systems based on the best ever result (Achievement Score) and the performance on the cardiopulmonary exercise test (Endurance Score). We identified the most important influencing factors using a neural network and characterized the strength of the variables using Shappley Additive Explanation (SHAP) values. Results We examined 1932 tests of 891 athletes and the AI analysis included 917 tests of 546 athletes (20.2±6.2 years, males: 397, 72.7%). Majority of the examined athletes were swimmers (27.4%), followed by basketball players (21.8%), water polo players (15.2%), handball players (13.3%) and football players (13.1%). The most important Achievement Score determinants were the weekly training hours (SHAP=0.27), training years (SHAP=0.26) and the age (SHAP=0.15). Meanwhile, Endurance Score was mainly affected by skeletal muscle mass (SHAP=0.85), weight (SHAP=0.6) and peak heart rate during exercise (SHAP=0.32). Our results were validated on a test population (N=102) and in the Achievement Score the mean absolute error (MAE) was 0.64, the determination coefficient (R2) was 0.56, in Endurance Score the MAE was 0.84 and the R2 was 0.71. Conclusion Based on our results, while endurance is mainly determined by the athlete’s physical characteristics, in athletic achievement experience is more important. With our scoring systems the athlete’s achievement and endurance can be predicted well.

European Heart JournalVol. 45(Supplement_1)
Semmelweis University (HU)
European Commission
Openalex Percentile: Top 71%
Diverse Approaches in Healthcare and Education Studies
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