# Machine Learning based prediction of Glucose Levels in Type 1 Diabetes Patients with the use of Continuous Glucose Monitoring Data

arXiv: https://arxiv.org/abs/2302.12856 · HTML: https://mlsanity.com/paper/2302.12856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 81 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [Explaining Recurrent Neural Network Predictions in Sentiment Analysis](https://mlsanity.com/paper/1706.07206/) (2017)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [Learning Natural Language Inference with LSTM](https://mlsanity.com/paper/1512.08849/) (2015)
- [The unreasonable effectiveness of the forget gate](https://mlsanity.com/paper/1804.04849/) (2018)
- [Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition](https://mlsanity.com/paper/1507.06947/) (2015)
- [Explaining and Interpreting LSTMs](https://mlsanity.com/paper/1909.12114/) (2019)
- [Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs](https://mlsanity.com/paper/1603.00423/) (2016)
- [DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News](https://mlsanity.com/paper/1912.10806/) (2019)
- [A dual mode adaptive basal-bolus advisor based on reinforcement learning](https://mlsanity.com/paper/1901.01816/) (2019)
- [Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction](https://mlsanity.com/paper/2101.06850/) (2021)
- [Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach](https://mlsanity.com/paper/2008.03825/) (2020)

## Built on by (most cited first, 1 of 1)

- [Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2502.14183/) (2025)

## Abstract (arXiv)

A task of vital clinical importance, within Diabetes management, is the prevention of hypo/hyperglycemic events. Increasingly adopted Continuous Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time insights into a patient's blood glucose concentrations. Leveraging advanced Machine Learning (ML) Models as methods of prediction of future glucose levels, gives rise to substantial quality of life improvements, as well as providing a vital tool for monitoring diabetes. A regression based prediction approach is implemented recursively, with a series of Machine Learning Models: Linear Regression, Hidden Markov Model, Long-Short Term Memory Network. By exploiting a patient's past 11 hours of blood glucose (BG) concentration measurements, a prediction of the 60 minutes is made. Results will be assessed using performance metrics including: Root Mean Squared Error (RMSE), normalised energy of the second-order differences (ESOD) and F1 score. Research of past and current approaches, as well as available dataset, led to the establishment of an optimal training methodology for the CITY dataset, which may be leveraged by future model development. Performance was aligned with similar state-of-art ML models, with LSTM having RMSE of 28.55, however no significant advantage was observed over classical Auto-regressive AR models. Compelling insights into LSTM prediction behaviour could increase public and legislative trust and understanding, progressing the certification of ML models in Artificial Pancreas Systems (APS).