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Predicting Blood Glucose Levels of Diabetes Patients
Type of publication: Mastersthesis
Type: Masters Thesis
Year: 2018
Month: January
School: TU Darmstadt
Abstract: Time series data is used for modelling, description, forecasting, and control in many fields from engineering to statistics. Time series forecasting is one of the domains of time series analysis, which requires regression. Along with the recent developments in deep learning techniques, the advancement in the technologies personal health care devices are making it possible to apply deep learning methods on the vast amounts of electronic health data. We aim to provide reliable blood glucose level prediction for diabetes patients so that the negative effects of the disease can be minimized. Currently, recurrent neural networks (RNNs), and in particular the long-short term memory unit (LSTM), are the state-of-the-art in timeseries forecasting. Alternatively, in this work we employ convolutional neural networks (CNNs) with multiple layers to predict future blood glucose level of a diabetes type 2 patient. Besides our CNN model, we also investigate whether our static insulin sensitivity calculation model’s results have a correlation with basal rate of the patient. We use the static insulin sensitivity data with our prediction model, in order to find out whether it contributes for a better prediction or not. Our experimental results demonstrate that calculated static insulin sensitivity values do not have any correlation with the basal rate. Our convolutional neural network model forecasts multivariate timeseries with multiple outputs including the blood glucose level with a 1.0729 mean absolute error for the prediction horizon of 15 minutes.
Authors Gülesir, Gizem
  • masterarbeit_gizem_gulesir.pdf