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<p>This paper considers the challenge of clinical reliability and generalization in heart disease diagnosis, where traditional ensembles often overlook feature heterogeneity and lack uncertainty quantification. We propose an uncertainty-aware feature-weighted ensemble (UAFE) framework designed for robust cardiovascular prediction. UAFE integrates three core components: (1) Feature Stratification to train specialized learners on importance-based subgroups; (2) Dynamic uncertainty weighting to adaptively adjust model contributions based on prediction disagreement; and (3) Neighborhood refinement to rectify decisions for high-uncertainty samples using local geometric priors. Comprehensive experiments on a multi-center dataset demonstrate that UAFE outperforms state-of-the-art baselines with an accuracy of 0.8660. Furthermore, leave-one-center-out (LOCO) validation across four international clinical sites shows that UAFE maintains superior stability (mean accuracy 0.8332) against institutional distribution shifts, establishing its effectiveness for deployment in unseen clinical environments.</p>

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Uncertainty-aware feature-weighted ensemble framework for heart disease prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Xinling Wang,
  • Yangrui Fan,
  • Minglian Yu,
  • Fujiang Yuan

摘要

This paper considers the challenge of clinical reliability and generalization in heart disease diagnosis, where traditional ensembles often overlook feature heterogeneity and lack uncertainty quantification. We propose an uncertainty-aware feature-weighted ensemble (UAFE) framework designed for robust cardiovascular prediction. UAFE integrates three core components: (1) Feature Stratification to train specialized learners on importance-based subgroups; (2) Dynamic uncertainty weighting to adaptively adjust model contributions based on prediction disagreement; and (3) Neighborhood refinement to rectify decisions for high-uncertainty samples using local geometric priors. Comprehensive experiments on a multi-center dataset demonstrate that UAFE outperforms state-of-the-art baselines with an accuracy of 0.8660. Furthermore, leave-one-center-out (LOCO) validation across four international clinical sites shows that UAFE maintains superior stability (mean accuracy 0.8332) against institutional distribution shifts, establishing its effectiveness for deployment in unseen clinical environments.