A Clustering-Based Multi-Layer Distributed Ensemble for Neurological Diagnostics in Cloud Services

1. 2020 – 2021
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A Clustering-Based Multi-Layer Distributed Ensemble for Neurological
Diagnostics in Cloud Services
Abstract
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 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 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-
outperformed all base classifiers and simpler counterpart models in all cloud
frameworks.