Original scientific paper
https://doi.org/10.1080/00051144.2023.2284031
Diabetes classification using MapReduce-based capsule network
G. Arun
; M.P.Nachimuthu M.Jaganathan Engineering College, Erode, India
*
C. N. Marimuthu
; Nandha Engineering College, Erode, India
* Corresponding author.
Abstract
Big data analytics is a complex exploratory process to uncover hidden data information from vast
collections of data. It often provides enormous information from diverse sources and the use of
analytics provides confined knowledge from the collected noisy data. In the case of diabetes
data, there exist a massive collection of patient data that relates to significant information on
patient health and its critical nature. In order of validating and analysing the data to get desired
information about a patient and their health risk from the vast collection of data, the study uses
bigdata based deep learning analytics. This study uses a Deep Learning Model namely capsule
network (CapsNet) is executed on a MapReduce framework. The CapsNet present in the MapReduce framework enables the classification of instances via proper regulations. This model after
suitable training with the training dataset enables optimal classification of instances to detect the
nature of the risk of a patient. The validation conducted on the test dataset shows that the proposed CapsNets-based MapReduce model obtains increased accuracy, recall, and F-score than
the conventional MapReduce and deep learning models.
Keywords
Capsnets; MapReduce; classification; big data; network; framework
Hrčak ID:
322947
URI
Publication date:
21.11.2023.
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