ItemArticle

Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture

Title
Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture
Creator
Pati A.; Parhi M.; Pattanayak B.K.; Singh D.; Samanta D.; Banerjee A.; Biring S.; Dalapati G.K.
Description
Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F-measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key. 2022 Abhilash Pati et al.
Source
Wireless Communications and Mobile Computing, Vol-2022
Date
2022-01-01
Publisher
Hindawi Limited
Coverage
Pati A., Siksha 'o' Anusandhan University, Odisha, Bhubaneswar, India; Parhi M., Siksha 'o' Anusandhan University, Odisha, Bhubaneswar, India; Pattanayak B.K., Siksha 'o' Anusandhan University, Odisha, Bhubaneswar, India; Singh D., Siksha 'o' Anusandhan University, Odisha, Bhubaneswar, India; Samanta D., Department of Computer Science, CHRIST University, Bangalore, India; Banerjee A., Physics Department, Bidhan Chandra College, Asansol, 713 303, India, Organic Electronics Research Center, Ming Chi University of Technology, New Taipei City, 24301, Taiwan; Biring S., Organic Electronics Research Center, Ming Chi University of Technology, New Taipei City, 24301, Taiwan, Department of Electronic Engineering, Ming Chi University of Technology, New Taipei City, 24301, Taiwan; Dalapati G.K., Organic Electronics Research Center, Ming Chi University of Technology, New Taipei City, 24301, Taiwan, Sunkonnect, 1 Cleantech Loop, Singapore, 637141, Singapore
Rights
All Open Access; Gold Open Access
Relation
ISSN: 15308669
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

Pati A.; Parhi M.; Pattanayak B.K.; Singh D.; Samanta D.; Banerjee A.; Biring S.; Dalapati G.K., “Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/15336.

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