KACQ-DCNN:不确定性意识可解释的科尔莫戈罗夫-阿诺德古典量子双通道神经网络用于心脏病检测
Md Abrar Jahin1, Md Akmol Masud2, M F Mridha3
1Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA; Physics and Biology Unit, Okinawa Institute of Science and Technology Graduate University (OIST), Okinawa, 904-0412, Japan.
Computers in biology and medicine
|August 27, 2025
概括
一个新的混合神经网络,Kolmogorov-Arnold经典量子双通道神经网络 (KACQ-DCNN),可以提高心脏病检测的准确性. 这种先进的模型为心血管诊断提供了更好的解释性和可靠的不确定性量化.
科学领域:
- 心血管诊断
- 人工智能
- 量子计算
背景情况:
- 心脏衰竭是全球的主要健康问题, 每年造成数百万人的死亡.
- 目前的诊断方法缺乏早期检测能力和有效的干预计划.
- 经典的机器学习模型难以处理复杂的数据,
研究的目的:
- 为改善心血管诊断开发一种新型的混合经典量子神经网络.
- 解决经典机器学习在处理复杂,高维数据方面的局限性.
- 通过量子计算提高心脏病检测的准确性和可解释性.
主要方法:
- 介绍科尔莫戈罗夫-阿诺德古典量子双通道神经网络 (KACQ-DCNN).
- 将科尔莫戈罗夫-阿诺德网络 (KAN) 组件与可学习激活函数的量子电路集成.
- 根据37个基准模型,使用4个量子位的1层KACQ-DCNN模型进行评估.
主要成果:
- KACQ-DCNN的准确度达到92.03%,ROC-AUC得分达到94.77%,比基准模型更高.
- 与多层感知器 (MLP) 变体相比,废除研究显示出协同效应,精度提高了约2%.
- 通过LIME和SHAP证明了心脏病检测的准确性和可解释的见解.
结论:
- KACQ-DCNN在心血管诊断方面取得了重大进展.
- 该模型提供可解释的洞察力和可靠的不确定性量化.
- 这种混合方法为心脏病学中更可靠和透明的临床决策铺平了道路.
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