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

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dc.contributor.authorMeijner, Christian
dc.contributor.authorPersson, Simon
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:35:14Z
dc.date.available2019-07-03T14:35:14Z
dc.date.issued2017
dc.description.abstractIn 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.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/251317
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleBlood Glucose Prediction for Type 1 Diabetes using Machine Learning Long Short-term Memory based models for blood glucose prediction
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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