Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Don Adjeroh

Committee Co-Chair

Gianfranco Doretto

Committee Member

Gianfranco Doretto

Committee Member

Jim Bardes


Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline for classifying blood glucose levels based on non-invasive biometric measurements. Starting with these non-invasive features this thesis develops a machine learning approach to classify the blood glucose levels using a training dataset. Classification is performed by adaptively applying the best classification modules for a given sample input sample. The dataset used was Pima Indians Diabetes (PID), which is a publically available multivariate dataset from the National Institute of Diabetes and Digestive and Kidney Diseases. Results from the approach demonstrate the feasibility of automated blood glucose level classification, using easy to acquire non-invasive measurements.


Code stored on google colab notebook: