Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use a population data. Our experimental results illustrate the effectiveness of the proposed model.
| Identifer | oai:union.ndltd.org:ndsu.edu/oai:library.ndsu.edu:10365/28179 |
| Date | January 2018 |
| Creators | Fernando, Warnakulasuriya Chandima |
| Publisher | North Dakota State University |
| Source Sets | North Dakota State University |
| Detected Language | English |
| Type | text/thesis |
| Format | application/pdf |
| Rights | NDSU Policy 190.6.2, https://www.ndsu.edu/fileadmin/policy/190.pdf |
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