What question did this study set out to answer?

The aim is to enhance athlete fatigue risk prediction using a novel meta-learning framework.

February 11, 2026Open Access

MLEF: a novel meta-learning framework with feature selection for enhanced athlete fatigue risk prediction

Puntos clave

The aim is to enhance athlete fatigue risk prediction using a novel meta-learning framework.
Developed a Meta-Learning Ensemble Framework (MLEF) with three layers: feature selection, base learners, and meta-learning integration.
Conducted feature selection using ANOVA F-statistic to identify key physiological indicators.
Trained logistic regression classifiers and combined their predictions with ensemble strategies.
Used the AFR-1000 dataset consisting of 1000 athletes for model training and testing.
Achieved 99.00% accuracy and 99.89% ROC-AUC, outperforming traditional machine learning and deep learning models.
Improved F1-score from 98.46% to 98.98% through progressive integration of ensemble methods.
Identified HRV, HeartRate_Recovery, and Cortisol_Level as significant predictors of fatigue risk.

Resumen

Abstract Athlete fatigue and overtraining are critical factors affecting performance and health, yet traditional evaluation methods relying on subjective judgment or single-indicator monitoring lack systematic and real-time capability. This study proposes a novel Meta-Learning Ensemble Framework (MLEF) integrating multidimensional physiological monitoring for intelligent fatigue risk prediction. The MLEF architecture consists of three progressive layers: a Feature Selection Layer using ANOVA F-statistic based univariate selection to identify the top 12 features from 15 original variables, a Base Learner Layer training four heterogeneous logistic regression classifiers with different regularization configurations, and a Meta-Learning Layer integrating predictions through weighted voting and stacking ensemble strategies. We constructed experiments on the AFR-1000 dataset containing 1000 athletes with balanced class distribution (51: 49 normal/fatigue), split 8: 2 into training and testing sets with stratified sampling. On the independent test set, MLEF achieved 99. 00% accuracy, 98. 98% F1-score, and 99. 89% ROC-AUC, significantly outperforming traditional machine learning methods (Logistic Regression 98. 50%, SVM 92. 50%, XGBoost 88. 00%) and deep learning models (Attention Network 97. 50%, DNN 97. 50%). Ablation experiments demonstrated that ANOVA F-statistic based feature selection maintained baseline performance while reducing dimensionality, and progressive ensemble integration raised F1-score from 98. 46% to 98. 98%. SHAP interpretability analysis identified HRV (mean |SHAP |=3. 95 | = 3. 95), HeartRateRecovery (2. 89), and CortisolLevel (2. 46) as top predictors, with HRV-Lactate interaction revealing synergistic amplification of fatigue risk. The MLEF model provides a practical AI tool for training monitoring with high accuracy and interpretability, offering scientific guidance for personalized training and recovery planning.

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