The conventional diagnostic process and tools of cardiovascular autonomic neuropathy (CAN) can easily identify the two main categories of the condition: severe/definite CAN and normal/healthy without CAN. Conventional techniques encounter significant challenges when identifying CAN in its early or atypical stages due to the inherent imbalanced and incompleteness condition in the collected clinical multimodal data, including electrocardiogram (ECG) data from ECG sensors, blood chemistry, podiatry, and endocrinology features. Therefore, most detection tools and techniques are limited to binary CAN classification. However, early diagnosis of CAN or diagnosis of the atypical stages of CAN is more important than the diagnosis of severe CAN, which, in fact, is easily identifiable with a few diagnostic reports. In this paper, we propose a novel multi-class classification approach for timely CAN detection. The proposed classification algorithm develops a multistage fusion model by combining feature selection and multimodal feature fusion techniques. The proposed method develops a performance criterion-based feature selection technique to guarantee highly significant features. A multimodal feature fusion technique was developed using deep learning feature fusion and selected original features. The experimental results obtained from testing with a large CAN dataset indicate that the proposed algorithm significantly improved the diagnostic accuracy of CAN compared to conventional Ewing battery features. The algorithm also identified the early or atypical stages of CAN with an AUC score of 0.931 using leave-one-out cross-validation.

心血管自主神经病变 (CAN) 的传统诊断过程和工具可以轻松识别两种主要类型的疾病：严重/明确的 CAN 和正常/健康的无 CAN。由于收集到的临床多模态数据（包括来自 ECG 传感器、血液化学、足病学和内分泌学特征的心电图 (ECG) 数据）中固有的不平衡和不完整情况，传统技术在识别早期或非典型阶段的 CAN 时遇到了重大挑战。因此，大多数检测工具和技术仅限于二进制 CAN 分类。然而，CAN的早期诊断或CAN非典型阶段的诊断比重度CAN的诊断更重要，事实上，通过一些诊断报告很容易识别。在本文中，我们提出了一种新颖的多类分类方法来及时检测 CAN。所提出的分类算法通过结合特征选择和多模态特征融合技术开发了多级融合模型。所提出的方法开发了一种基于性能标准的特征选择技术，以保证高度重要的特征。使用深度学习特征融合和选择的原始特征开发了一种多模态特征融合技术。使用大型 CAN 数据集进行测试获得的实验结果表明，与传统的 Ewing 电池功能相比，所提出的算法显着提高了 CAN 的诊断准确性。该算法还使用留一法交叉验证确定了 CAN 的早期或非典型阶段，AUC 得分为 0.931。