IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes

Diabetes is a chronic disease that can affect human health negatively when the glucose levels in the blood are elevated over the creatin range called hyperglycemia. The current devices for continuous glucose monitoring (CGM) supervise the glucose level in the blood and alert user to the type-1 Diabe...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ahmed R. Nasser, Ahmed M. Hasan, Amjad J. Humaidi, Ahmed Alkhayyat, Laith Alzubaidi, Mohammed A. Fadhel, José Santamaría, Ye Duan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence deep learning blood glucose level prediction type-1 diabetes cloud computing IoT Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/7ed635a4da224aab836357249f4c8bd0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7ed635a4da224aab836357249f4c8bd0
record_format	dspace
spelling	oai:doaj.org-article:7ed635a4da224aab836357249f4c8bd02021-11-11T15:42:39ZIoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes10.3390/electronics102127192079-9292https://doaj.org/article/7ed635a4da224aab836357249f4c8bd02021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2719https://doaj.org/toc/2079-9292Diabetes is a chronic disease that can affect human health negatively when the glucose levels in the blood are elevated over the creatin range called hyperglycemia. The current devices for continuous glucose monitoring (CGM) supervise the glucose level in the blood and alert user to the type-1 Diabetes class once a certain critical level is surpassed. This can lead the body of the patient to work at critical levels until the medicine is taken in order to reduce the glucose level, consequently increasing the risk of causing considerable health damages in case of the intake is delayed. To overcome the latter, a new approach based on cutting-edge software and hardware technologies is proposed in this paper. Specifically, an artificial intelligence deep learning (DL) model is proposed to predict glucose levels in 30 min horizons. Moreover, Cloud computing and IoT technologies are considered to implement the prediction model and combine it with the existing wearable CGM model to provide the patients with the prediction of future glucose levels. Among the many DL methods in the state-of-the-art (SoTA) have been considered a cascaded RNN-RBM DL model based on both recurrent neural networks (RNNs) and restricted Boltzmann machines (RBM) due to their superior properties regarding improved prediction accuracy. From the conducted experimental results, it has been shown that the proposed Cloud&DL-based wearable approach achieves an average accuracy value of 15.589 in terms of RMSE, then outperforms similar existing blood glucose prediction methods in the SoTA.Ahmed R. NasserAhmed M. HasanAmjad J. HumaidiAhmed AlkhayyatLaith AlzubaidiMohammed A. FadhelJosé SantamaríaYe DuanMDPI AGarticleartificial intelligencedeep learningblood glucose level predictiontype-1 diabetescloud computingIoTElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2719, p 2719 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence deep learning blood glucose level prediction type-1 diabetes cloud computing IoT Electronics TK7800-8360
spellingShingle	artificial intelligence deep learning blood glucose level prediction type-1 diabetes cloud computing IoT Electronics TK7800-8360 Ahmed R. Nasser Ahmed M. Hasan Amjad J. Humaidi Ahmed Alkhayyat Laith Alzubaidi Mohammed A. Fadhel José Santamaría Ye Duan IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes
description	Diabetes is a chronic disease that can affect human health negatively when the glucose levels in the blood are elevated over the creatin range called hyperglycemia. The current devices for continuous glucose monitoring (CGM) supervise the glucose level in the blood and alert user to the type-1 Diabetes class once a certain critical level is surpassed. This can lead the body of the patient to work at critical levels until the medicine is taken in order to reduce the glucose level, consequently increasing the risk of causing considerable health damages in case of the intake is delayed. To overcome the latter, a new approach based on cutting-edge software and hardware technologies is proposed in this paper. Specifically, an artificial intelligence deep learning (DL) model is proposed to predict glucose levels in 30 min horizons. Moreover, Cloud computing and IoT technologies are considered to implement the prediction model and combine it with the existing wearable CGM model to provide the patients with the prediction of future glucose levels. Among the many DL methods in the state-of-the-art (SoTA) have been considered a cascaded RNN-RBM DL model based on both recurrent neural networks (RNNs) and restricted Boltzmann machines (RBM) due to their superior properties regarding improved prediction accuracy. From the conducted experimental results, it has been shown that the proposed Cloud&DL-based wearable approach achieves an average accuracy value of 15.589 in terms of RMSE, then outperforms similar existing blood glucose prediction methods in the SoTA.
format	article
author	Ahmed R. Nasser Ahmed M. Hasan Amjad J. Humaidi Ahmed Alkhayyat Laith Alzubaidi Mohammed A. Fadhel José Santamaría Ye Duan
author_facet	Ahmed R. Nasser Ahmed M. Hasan Amjad J. Humaidi Ahmed Alkhayyat Laith Alzubaidi Mohammed A. Fadhel José Santamaría Ye Duan
author_sort	Ahmed R. Nasser
title	IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes
title_short	IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes
title_full	IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes
title_fullStr	IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes
title_full_unstemmed	IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes
title_sort	iot and cloud computing in health-care: a new wearable device and cloud-based deep learning algorithm for monitoring of diabetes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7ed635a4da224aab836357249f4c8bd0
work_keys_str_mv	AT ahmedrnasser iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes AT ahmedmhasan iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes AT amjadjhumaidi iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes AT ahmedalkhayyat iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes AT laithalzubaidi iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes AT mohammedafadhel iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes AT josesantamaria iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes AT yeduan iotandcloudcomputinginhealthcareanewwearabledeviceandcloudbaseddeeplearningalgorithmformonitoringofdiabetes
_version_	1718434095184216064

IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes

Ejemplares similares