Blood Glucose Prediction for Type 1 Diabetes using Machine Learning Long Short-term Memory based models for blood glucose prediction

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/251317
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Type: Examensarbete för masterexamen
Master Thesis
Title: Blood Glucose Prediction for Type 1 Diabetes using Machine Learning Long Short-term Memory based models for blood glucose prediction
Authors: Meijner, Christian
Persson, Simon
Abstract: In this thesis, walk forward testing is used to evaluate the performance of two long short-term memory (LSTM) models for predicting blood glucose values for patients with type 1 diabetes. The models are compared with a support vector regression (SVR) model as well as with an auto regressive integrated moving average (ARIMA) model, both of which have been used in related research within the area. The best performing long short-term model produces results equal to those of the SVR model and it outperforms the ARIMA model for all prediction horizons. In contrast to models in related research, this LSTM model also has the ability to assign a level of confidence to each prediction, adding an edge in practical usability.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/251317
Collection:Examensarbeten för masterexamen // Master Theses



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