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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | GOEL, PRANAY | - |
dc.contributor.author | ., PRAJJWAL | - |
dc.date.accessioned | 2023-03-01T05:06:05Z | - |
dc.date.available | 2023-03-01T05:06:05Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.citation | 62 | en_US |
dc.identifier.uri | http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7639 | - |
dc.description.abstract | Continuous Glucose Monitoring (CGM) is a cutting-edge method for monitoring blood glucose levels at predetermined intervals. Type 2 diabetes is a long-term chronic lifestyle disease brought on by high blood sugar levels. In our investigation, we will use isolated liquid meals and CGM data to predict the glucose level inspiring from a widely used fitting method that is described in this work. Owing to the CGM data’s signifi-cant nonlinearity and the complexity of adequately modelling it, we restricted the use of non-linear approaches to a single time period during the day. After rigorous research, we discovered that we can draw inspiration from the underdamped case of a damped harmonic oscillator to simulate our data accurately. We then used the same technique to apply and model CGM data available for five patients (2 non-diabetic, 2 diabetic and one pre-diabetic). We have carefully analyzed and shown the data, day by day as well, so that it may be used for more comprehensive analyses of the glucose dynamics and the prescription of a diet or medication for diabetic patients since it demonstrates how glucose varies with simple liquid meals. Keywords: Non-Linear modelling, Data visualization and analysis, Continuous Glucose Monitoring, Harmonic Oscillator | en_US |
dc.language.iso | en | en_US |
dc.subject | Sparse modeling | en_US |
dc.subject | Data Science | en_US |
dc.subject | Non linear Dynamics | en_US |
dc.subject | Healthcare | en_US |
dc.subject | CGM | en_US |
dc.subject | Data visualization | en_US |
dc.subject | Data Analysis | en_US |
dc.title | Investigating sparse model for Continuous Glucose Monitoring in Type 2 Diabetes | en_US |
dc.type | Thesis | en_US |
dc.type | Dissertation | en_US |
dc.description.embargo | no embargo | en_US |
dc.type.degree | MS-exit | en_US |
dc.contributor.department | Dept. of Physics | en_US |
dc.contributor.registration | 20192033 | en_US |
Appears in Collections: | MS THESES |
Files in This Item:
File | Description | Size | Format | |
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20192033_PRAJJWAL_MS_Thesis.pdf | MS Thesis | 3.67 MB | Adobe PDF | View/Open |
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