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· One min read

Dear Colleagues,

Currently, we are in the process of editing a forthcoming book entitled "Metaverse Applications for New Business Models and Disruptive Innovation", to be published by IGI Global, an international publisher of progressive academic research.

We would like to take this opportunity to cordially invite you all to submit your work for consideration in this book. We are certain that your contribution on this topic and/or other related research areas would make an excellent addition to this book. Please visit https://www.igi-global.com/publish/call-for-papers/call-details/5848 for more details regarding this book and to submit your work. You may kindly use the following link to submit a chapter: https://www.igi-global.com/publish/call-for-papers/submit/5848

If you have any questions or concerns, please do not hesitate to contact us. Thank you very much for your consideration of this invitation, and I hope to hear from you by 4th April 2022.

Best wishes,
Muhammad Anshari (Universiti Brunei Darussalam, #bruneidarussalam)
Muhammad Syafrudin (Sejong University, #korea)
Ganjar Alfian (Universitas Gadjah Mada (UGM), #indonesia)
Editors

· 2 min read

Abstract

Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domain attributes to predict blood glucose levels 15, 30, 45 and 60 min in the future. Initially, the model’s features are selected based on the previous 30 min of BG measurements before a trained model is generated for each patient. These features are combined with time-domain attributes to give additional inputs to the proposed ANN. The prediction model was tested on 12 patients with Type 1 diabetes (T1D) and the results were compared with other data-driven models including the Support Vector Regression (SVR), K-Nearest Neighbor (KNN), C4.5 Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) models. Our results show that the proposed BG prediction model that is based on an ANN outperformed all other models with an average Root Mean Square Error (RMSE) of 2.82, 6.31, 10.65 and 15.33 mg/dL for Prediction Horizons (PHs) of 15, 30, 45 and 60 min, respectively. Our testing showed that combining time-domain attributes into the input data resulted in enhanced performance of majority of prediction models. The implementation of proposed prediction model allows patients to obtain future blood glucose levels, so that the preventive alerts can be generated before critical hypoglycemic/ hyperglycemic events occur.

Published in: Biocybernetics and Biomedical Engineering
DOI: 10.1016/j.bbe.2020.10.004