📅 11 October 2025
DOI: 10.62411/jcta.14873
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
🔖 Keywords
#AdaBoost; Attention Mechanism; Deep Learning; Feature Fusion; Medical Diagnosis; Quantum Machine Learning; Structured Data; Unsupervised LSTM
ℹ️ Informasi Publikasi
Tanggal Publikasi
11 October 2025
Volume / Nomor / Tahun
Volume 3, Nomor 2, Tahun 2025
📝 HOW TO CITE
Kusuma, Muh Galuh Surya Putra; Setiadi, De Rosal Ignatius Moses; Herowati, Wise; Sutojo, T.; Adi, Prajanto Wahyu; Dutta, Pushan Kumar; Nguyen, Minh T., "Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction," Journal of Computing Theories and Applications, vol. 3, no. 2, Oct. 2025.
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