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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/7090 |
| _version_ | 1827677898466656256 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:53:09Z |
| format | Article |
| id | doaj.art-a7667c8e64384beebd5f8806e8064a81 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:09Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a7667c8e64384beebd5f8806e8064a812023-11-22T21:36:26ZengMDPI AGSensors1424-82202021-10-012121709010.3390/s21217090Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with DiabetesFélix Tena0Oscar Garnica1Juan Lanchares2Jose Ignacio Hidalgo3Department of Computer Architecture, Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Computer Architecture, Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Computer Architecture, Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, SpainDepartment of Computer Architecture, Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, SpainThis 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.https://www.mdpi.com/1424-8220/21/21/7090deep learningneural networksensemble modelsdiabetesblood glucose prediction |
| spellingShingle | 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 Sensors deep learning neural networks ensemble models diabetes blood glucose prediction |
| title | 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_short | 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 |
| topic | deep learning neural networks ensemble models diabetes blood glucose prediction |
| url | https://www.mdpi.com/1424-8220/21/21/7090 |
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