Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes

This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons—30, 60, and 120 min—and compares their performance with ten recently proposed neural networks. The twelve models’ performances are evaluated under the same OhioT1DM Data...

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Autores principales:	Félix Tena, Oscar Garnica, Juan Lanchares, Jose Ignacio Hidalgo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning neural networks ensemble models diabetes blood glucose prediction Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/a7667c8e64384beebd5f8806e8064a81
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id	oai:doaj.org-article:a7667c8e64384beebd5f8806e8064a81
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spelling	oai:doaj.org-article:a7667c8e64384beebd5f8806e8064a812021-11-11T19:06:19ZEnsemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes10.3390/s212170901424-8220https://doaj.org/article/a7667c8e64384beebd5f8806e8064a812021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7090https://doaj.org/toc/1424-8220This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons—30, 60, and 120 min—and compares their performance with ten recently proposed neural networks. The twelve models’ performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, and tools at the three prediction horizons using the most common metrics in blood glucose prediction, and we rank the best-performing ones using three methods devised for the statistical comparison of the performance of multiple algorithms: scmamp, model confidence set, and superior predictive ability. Our analysis provides a comparison of the state-of-the-art neural networks for blood glucose prediction, estimating the model’s error, highlighting those with the highest probability of being the best predictors, and providing a guide for their use in clinical practice.Félix TenaOscar GarnicaJuan LancharesJose Ignacio HidalgoMDPI AGarticledeep learningneural networksensemble modelsdiabetesblood glucose predictionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7090, p 7090 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning neural networks ensemble models diabetes blood glucose prediction Chemical technology TP1-1185
spellingShingle	deep learning neural networks ensemble models diabetes blood glucose prediction Chemical technology TP1-1185 Félix Tena Oscar Garnica Juan Lanchares Jose Ignacio Hidalgo Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
description	This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons—30, 60, and 120 min—and compares their performance with ten recently proposed neural networks. The twelve models’ performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, and tools at the three prediction horizons using the most common metrics in blood glucose prediction, and we rank the best-performing ones using three methods devised for the statistical comparison of the performance of multiple algorithms: scmamp, model confidence set, and superior predictive ability. Our analysis provides a comparison of the state-of-the-art neural networks for blood glucose prediction, estimating the model’s error, highlighting those with the highest probability of being the best predictors, and providing a guide for their use in clinical practice.
format	article
author	Félix Tena Oscar Garnica Juan Lanchares Jose Ignacio Hidalgo
author_facet	Félix Tena Oscar Garnica Juan Lanchares Jose Ignacio Hidalgo
author_sort	Félix Tena
title	Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
title_short	Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
title_full	Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
title_fullStr	Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
title_full_unstemmed	Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes
title_sort	ensemble models of cutting-edge deep neural networks for blood glucose prediction in patients with diabetes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a7667c8e64384beebd5f8806e8064a81
work_keys_str_mv	AT felixtena ensemblemodelsofcuttingedgedeepneuralnetworksforbloodglucosepredictioninpatientswithdiabetes AT oscargarnica ensemblemodelsofcuttingedgedeepneuralnetworksforbloodglucosepredictioninpatientswithdiabetes AT juanlanchares ensemblemodelsofcuttingedgedeepneuralnetworksforbloodglucosepredictioninpatientswithdiabetes AT joseignaciohidalgo ensemblemodelsofcuttingedgedeepneuralnetworksforbloodglucosepredictioninpatientswithdiabetes
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Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes

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