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Early cardiovascular disease detection using hierarchical quantum ensemble model.

Kian Lun Soon1, Wai Leong Pang1, Hui Hwang Goh1

  • 1School of Engineering and Centre for Sustainable Societies, Taylor's University, Subang Jaya, Selangor, Malaysia.

Computer Methods in Biomechanics and Biomedical Engineering
|January 10, 2026
PubMed
Summary

A novel Hierarchical Quantum Ensemble Model (HQEM) improves cardiovascular disease (CVD) classification accuracy. This advanced AI approach effectively processes complex patient data, achieving 97% accuracy and 98% AUC for better diagnostics.

Keywords:
LightGBMQuantum Neural NetworkXGBoostcardiovascular disease

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Area of Science:

  • Artificial Intelligence in Medicine
  • Quantum Computing Applications
  • Machine Learning for Healthcare

Background:

  • Light Gradient Boosting Machine (LightGBM) faces challenges with heterogeneous cardiovascular disease (CVD) data.
  • Existing models may struggle to capture complex, non-linear patterns in biomedical datasets.
  • Accurate CVD classification is crucial for timely and effective patient treatment.

Purpose of the Study:

  • To introduce a Hierarchical Quantum Ensemble Model (HQEM) to overcome LightGBM limitations in CVD data processing.
  • To enhance the feature representation for improved classification performance.
  • To develop a robust model for accurate cardiovascular disease detection.

Main Methods:

  • Proposed a novel Hierarchical Quantum Ensemble Model (HQEM) architecture.
  • Utilized a Quantum Neural Network (QNN) and eXtreme Gradient Boosting (XGBoost) as parallel base classifiers.
  • Employed a LightGBM meta-classifier on the enriched feature space generated by the base classifiers.

Main Results:

  • Achieved 97% accuracy in cardiovascular disease classification.
  • Obtained an Area Under the Curve (AUC) of 98%.
  • Demonstrated superior efficacy in handling complex feature distributions compared to traditional methods.

Conclusions:

  • The HQEM model significantly enhances the accuracy and robustness of CVD classification.
  • Quantum-inspired ensemble methods show great promise for complex biomedical data analysis.
  • HQEM offers a powerful new tool for improving cardiovascular diagnostics through advanced machine learning.