Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach

Autor:	Hoda Nemat, Heydar Khadem, Mohammad R. Eissa, Jackie Elliott, Mohammed Benaissa
Rok vydání:	2022
Předmět:	Blood Glucose Machine Learning Benchmarking ComputingMethodologies_PATTERNRECOGNITION Diabetes Mellitus Type 1 Health Information Management Humans Health Informatics Electrical and Electronic Engineering Algorithms Computer Science Applications
Zdroj:	IEEE journal of biomedical and health informatics. 26(6)
ISSN:	2168-2208 2168-2194
Popis:	Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb3c762ca13a7613a0a79f54c8d3ac06 https://pubmed.ncbi.nlm.nih.gov/35077372 Zobrazit plný text záznamu