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

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 ), HeartRate_Recovery (2.89), and Cortisol_Level (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.