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Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data
Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist th...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856798/ https://www.ncbi.nlm.nih.gov/pubmed/35187166 http://dx.doi.org/10.1155/2022/5038851 |
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author | Hannah, S. Deepa, A. J. Chooralil, Varghese S. BrillySangeetha, S. Yuvaraj, N. Arshath Raja, R. Suresh, C. Vignesh, Rahul YasirAbdullahR, Srihari, K. Alene, Assefa |
author_facet | Hannah, S. Deepa, A. J. Chooralil, Varghese S. BrillySangeetha, S. Yuvaraj, N. Arshath Raja, R. Suresh, C. Vignesh, Rahul YasirAbdullahR, Srihari, K. Alene, Assefa |
author_sort | Hannah, S. |
collection | PubMed |
description | Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559. |
format | Online Article Text |
id | pubmed-8856798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-88567982022-02-19 Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data Hannah, S. Deepa, A. J. Chooralil, Varghese S. BrillySangeetha, S. Yuvaraj, N. Arshath Raja, R. Suresh, C. Vignesh, Rahul YasirAbdullahR, Srihari, K. Alene, Assefa Biomed Res Int Research Article Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559. Hindawi 2022-02-11 /pmc/articles/PMC8856798/ /pubmed/35187166 http://dx.doi.org/10.1155/2022/5038851 Text en Copyright © 2022 S. Hannah et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Hannah, S. Deepa, A. J. Chooralil, Varghese S. BrillySangeetha, S. Yuvaraj, N. Arshath Raja, R. Suresh, C. Vignesh, Rahul YasirAbdullahR, Srihari, K. Alene, Assefa Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data |
title | Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data |
title_full | Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data |
title_fullStr | Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data |
title_full_unstemmed | Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data |
title_short | Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data |
title_sort | blockchain-based deep learning to process iot data acquisition in cognitive data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856798/ https://www.ncbi.nlm.nih.gov/pubmed/35187166 http://dx.doi.org/10.1155/2022/5038851 |
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