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<p>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<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(|=3.95\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">|</mo> <mo>=</mo> <mn>3.95</mn> </mrow> </math></EquationSource> </InlineEquation>), 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月7日

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  • Dejin Wang,
  • Yuxi Peng,
  • Junhui Zhu,
  • Qihao Liu,
  • Taiquan Wang,
  • Shaoxue Wu,
  • Jueliang Tian,
  • Yule Yang,
  • Fang Tan

摘要

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.