KQML-Liver：结合 Kolmogorov-Arnold 网络的可解释混合量子机器学习肝病分类
KQML-Liver: Interpretable Hybrid Quantum Machine Learning with Kolmogorov-Arnold Networks for Liver Disease Classification
AI 导读
KQML-Liver 是一个将量子机器学习与 Kolmogorov-Arnold 网络（KAN）结合的混合框架，用参数化量子电路做特征表示、KAN 映射捕捉非线性关系并保持可解释性。
正文 · 原文
Abstract:Liver disease remains a major global health concern, highlighting the need for reliable and accessible early detection methods. Although recent advances in machine learning, deep learning and quantum machine learning (QML) have introduced promising diagnostic tools, many approaches suffer from limited interpretability and inflated performance caused by data leakage or overly simplistic evaluation protocols. These constraints reduce their reliability in real-world clinical practice. This paper proposes KQML-Liver, a hybrid framework that integrates QML with Kolmogorov-Arnold Networks (KANs) to enhance predictive performance and interpretability. The architecture combines parameterized quantum circuits for expressive feature representation with KAN-based functional mappings that capture nonlinear relationships in an interpretable manner. Evaluated on the Indian Liver Patient Dataset (ILPD) under a rigorous preprocessing protocol preventing data leakage, the proposed model achieves 82% classification accuracy and 100% recall, detecting all positive cases, outperforming existing baselines while providing improved transparency in clinical predictions. These results suggest that hybrid KAN-quantum architectures offer a promising direction for interpretable AI-assisted medical diagnosis.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15241 [cs.LG] |
| (or arXiv:2611.15241v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15241 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Long Nguyen Nhat [view email]
[v1]
Wed, 11 Nov 2026 09:31:35 UTC (3,487 KB)
来源：arXiv cs.AI · arxiv.org