Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability.

ABSTRACT: Heart rate variability (HRV) is a non-invasive health and fitness indicator, and machine learning (ML) has emerged as a powerful tool for analysing large HRV datasets. This study aims to identify athletic characteristics using the HRV test and ML algorithms. Two models were developed: Model 1 (M1) classified athletes and non-athletes using 856 observations from high-performance athletes and 494 from non-athletes. Model 2 (M2) identified an individual soccer player within a team based on 105 observations from the player and 514 from other team members. Three ML algorithms were applied -Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)- and SHAP values were used to interpret the results. In M1, the SVM algorithm achieved the highest performance (accura

SUBMITTER: Estrella T 

PROVIDER: S-EPMC11860660 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature