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<p>Cardiovascular diseases (CVDs) remain a major global health challenge, requiring early-detection models that are both accurate and generalizable across diverse data settings. This study introduces a preprocessing-enhanced stacking ensemble for binary CVD prediction, explicitly designed to improve robustness under heterogeneous feature distributions. The preprocessing pipeline incorporates feature transformation, derived attribute construction, encoding, and K-modes clustering, all applied after strict train–test separation to preserve evaluation validity. The proposed stacking architecture integrates three complementary tree-based base learners i.e., Random Forest, Decision Tree, and Extra Trees with Logistic Regression as a meta-learner to aggregate out-of-fold predictions. The framework was evaluated on three heterogeneous datasets. The ensemble achieved accuracies of 93.26%, 72%, and 99% on Datasets I, II, and III, respectively, with performance stability confirmed using 95% confidence intervals across five random seeds. Statistical significance analysis using McNemar’s test demonstrated that the proposed model significantly outperformed several strong baselines (<i>p</i> &lt; 0.05), including Random Forest, Logistic Regression, and XGBoost on Dataset I; CNN on Dataset II; and Decision Tree and Logistic Regression on Dataset III. These results indicate that the proposed framework maintains consistent performance across varying data modalities, noise levels, and feature structures.</p>

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A preprocessing-enhanced stacking classifier for generalized cardiovascular disease detection across diverse datasets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • Adeel Ashraf,
  • Adven Masih,
  • Aysha Saddiqa,
  • Jabar Mahmood,
  • Aitzaz Ali,
  • Mohamed Shabbir Hamza Abdulnabi,
  • Daniel Musafiri Balungu,
  • Xu Ying

摘要

Cardiovascular diseases (CVDs) remain a major global health challenge, requiring early-detection models that are both accurate and generalizable across diverse data settings. This study introduces a preprocessing-enhanced stacking ensemble for binary CVD prediction, explicitly designed to improve robustness under heterogeneous feature distributions. The preprocessing pipeline incorporates feature transformation, derived attribute construction, encoding, and K-modes clustering, all applied after strict train–test separation to preserve evaluation validity. The proposed stacking architecture integrates three complementary tree-based base learners i.e., Random Forest, Decision Tree, and Extra Trees with Logistic Regression as a meta-learner to aggregate out-of-fold predictions. The framework was evaluated on three heterogeneous datasets. The ensemble achieved accuracies of 93.26%, 72%, and 99% on Datasets I, II, and III, respectively, with performance stability confirmed using 95% confidence intervals across five random seeds. Statistical significance analysis using McNemar’s test demonstrated that the proposed model significantly outperformed several strong baselines (p < 0.05), including Random Forest, Logistic Regression, and XGBoost on Dataset I; CNN on Dataset II; and Decision Tree and Logistic Regression on Dataset III. These results indicate that the proposed framework maintains consistent performance across varying data modalities, noise levels, and feature structures.