Blood Glucose Level Prediction in Type 1 Diabetes Using Machine Learning

TL;DR

This paper explores machine learning techniques, including deep neural networks and ensemble methods, to predict blood glucose levels in Type 1 Diabetes patients at 30-minute intervals, aiming to improve diabetes management.

Contribution

It introduces the application of advanced machine learning models and ensemble strategies for blood glucose prediction using the latest DiaTrend dataset, enhancing prediction accuracy.

Findings

Deep neural networks outperform traditional models in prediction accuracy.

Ensemble methods like voting and stacking improve model robustness.

Models perform well across various glycemic conditions.

Abstract

Type 1 Diabetes is a chronic autoimmune condition in which the immune system attacks and destroys insulin-producing beta cells in the pancreas, resulting in little to no insulin production. Insulin helps glucose in your blood enter your muscle, fat, and liver cells so they can use it for energy or store it for later use. If insulin is insufficient, it causes sugar to build up in the blood and leads to serious health problems. People with Type 1 Diabetes need synthetic insulin every day. In diabetes management, continuous glucose monitoring is an important feature that provides near real-time blood glucose data. It is useful in deciding the synthetic insulin dose. In this research work, we used machine learning tools, deep neural networks, deep reinforcement learning, and voting and stacking regressors to predict blood glucose levels at 30-min time intervals using the latest DiaTrend dataset. Predicting blood glucose levels is useful in better diabetes management systems. The trained models were compared using several evaluation metrics. Our evaluation results demonstrate the performance of various models across different glycemic conditions for blood glucose prediction. The source codes of this work can be found in: https://github.com/soon-jynn-chu/t1d_bg_prediction