TY  - GEN 
AU  - Bernardez-Vilaboa, Ricardo
AU  - Povedano-Montero, F. Javier
AU  - Trillo-Vílchez, José Ramón
AU  - Ruiz-Pomeda, Alicia
AU  - Martínez-Florentín, Gema
AU  - Cedrún-Sánchez, Juan E.
PY  - 2025
UR  - https://hdl.handle.net/10481/106168
AB  - Background/Objective: This study aims to evaluate the predictive performance of three
supervised machine learning algorithms—decision tree (DT), support vector machine
(SVM), and k-nearest neighbors (KNN) in forecasting key visual skills relevant to...
LA  - eng
PB  - MDPI
KW  - eye tracking
KW  - machine learning
KW  - visual performance
KW  - rhythmic gymnastics
KW  - decision tree classification
KW  - biomedical optics
TI  - AI-Based Prediction of Visual Performance in Rhythmic Gymnasts Using Eye-Tracking Data and Decision Tree Models
DO  - 10.3390/photonics12070711
ER  -