KACQ-DCNN: Uncertainty-Aware Interpretable Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network for Heart Disease Detection

2024-10-09

arXiv.org

AI Summary

To address high misdiagnosis/missed-diagnosis rates, class imbalance, model opacity, and difficulty in uncertainty quantification in heart failure diagnosis, this paper proposes an interpretable hybrid classical–quantum neural network with uncertainty quantification. Methodologically, it pioneers the replacement of conventional multilayer perceptrons (MLPs) with Kolmogorov–Arnold networks (KANs) to enable learnable univariate activation functions, and couples this with a 4-qubit quantum channel to form a dual-channel collaborative modeling framework. The architecture integrates LIME and SHAP for local interpretability and conformal prediction for rigorous uncertainty quantification. Evaluated on high-dimensional, imbalanced electrocardiographic data, the model achieves 92.03% accuracy, 92.00% macro-F1 score, and 94.77% ROC-AUC—significantly outperforming 37 baselines. Ablation studies confirm that the dual-channel synergy contributes approximately 2% performance gain.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Heart failure is a leading cause of global mortality, necessitating improved diagnostic strategies. Classical machine learning models struggle with challenges such as high-dimensional data, class imbalances, poor feature representations, and lack of interpretability. While quantum machine learning holds promise, current hybrid models have not fully exploited quantum advantages. In this paper, we propose the Kolmogorov-Arnold Classical-Quantum Dual-Channel Neural Network (KACQ-DCNN), a novel hybrid architecture that replaces traditional multilayer perceptrons with Kolmogorov-Arnold Networks (KANs), enabling learnable univariate activation functions. Our KACQ-DCNN 4-qubit, 1-layer model outperforms 37 benchmark models, including 16 classical and 12 quantum neural networks, achieving an accuracy of 92.03%, with macro-average precision, recall, and F1 scores of 92.00%. It also achieved a ROC-AUC of 94.77%, surpassing other models by significant margins, as validated by paired t-tests with a significance threshold of 0.0056 (after Bonferroni correction). Ablation studies highlight the synergistic effect of classical-quantum integration, improving performance by about 2% over MLP variants. Additionally, LIME and SHAP explainability techniques enhance feature interpretability, while conformal prediction provides robust uncertainty quantification. Our results demonstrate that KACQ-DCNN improves cardiovascular diagnostics by combining high accuracy with interpretability and uncertainty quantification.

Problem

• Quantum Computing
• Cardiovascular Disease Detection
• Machine Learning Limitations

Innovation

• KACQ-DCNN
• Quantum-Classical Integration
• Uncertainty Assessment