Predicting Blood Glucose Levels Using LSTM

This study explores the application of Long Short-Term Memory algorithms to predict blood glucose levels, leveraging sequence-based data from a Dexcom G6 Continuous Glucose Monitoring sensor. This research methodically details a rigorous data cleansing process that transforms raw glucose readings into meaningful, sequenced input for Long Short-Term Memory models. By feeding the Long Short-Term Memory with structured data in a variety of volumes, it was analyzed and discussed how the quantity of information influences the accuracy of glucose level predictions. This investigation holds significant implications for the advancement of personalized glycemic control, potentially improving the day-to-day management and overall quality of life for individuals living with diabetes.