Early cardiovascular disease detection using hierarchical quantum ensemble model

Computer Methods in Biomechanics & Biomedical Engineering 2026

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Authors: Kian Lun Soon, Wai Leong Pang, Hui Hwang Goh, Yee Wai Sim, Swee King Phang, Hui Leng. Choo, Lam Tatt Soon, Nai Shyan Lai
DOI: 10.1080/10255842.2025.2612536
Cited by: 1
References: 34

Abstract

To mitigate the limitations of Light Gradient Boosting Machine (LightGBM) in processing heterogeneous cardiovascular disease (CVD) data, a Hierarchical Quantum Ensemble Model (HQEM) is proposed. This architecture deploys a Quantum Neural Network (QNN) and eXtreme Gradient Boosting (XGBoost) as parallel base classifiers to capture non-linear quantum patterns and sequential gradient trends. The resulting ensemble outputs enrich the feature space for a LightGBM meta-classifier. Validation across integrated datasets yielded 97% accuracy and a 98% Area Under the Curve (AUC), demonstrating the model's superior efficacy in handling complex feature distributions for robust CVD classification.

Fields of Study & Subjects

Quantum Computing Algorithms and Architecture Artificial Intelligence in Healthcare Quantum-Dot Cellular Automata

Publication details

Journal / source
Computer Methods in Biomechanics & Biomedical Engineering
Publisher
Informa UK Limited
Pages
1–18
Publication date
10 January 2026
Language
EN