This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks.

中文翻译：

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用于云服务中神经诊断的基于聚类的多层分布式集成

本文研究了在心脏自主神经病变 (CAN) 的神经学诊断过程中最小化云的不同数据中心之间的数据传输问题。这个问题以前在文献中从未考虑过。之前考虑用于 CAN 诊断的所有分类器都假设可以完全访问所有数据，如果将此类分类器部署在云中，这将导致训练期间数据传输的巨大负担。我们引入了一种基于聚类的多层分布式集成（CBMLDE）的新模型。它旨在消除在不同数据中心之间传输数据以训练分类器的需要。我们利用源自广泛的 DiScRi 数据库的数据集进行了实验。我们的综合测试确定了用于设置 CBMLDE 分类器的最佳选项组合。结果表明，CBMLDE 分类器不仅完全消除了患者数据传输的需要，而且在所有云框架中都显着优于所有基本分类器和更简单的对应模型。