Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction

• URL: http://arxiv.org/abs/2307.01238v1
• Date: Mon, 3 Jul 2023 12:22:04 GMT
• Title: Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction
• Authors: Daniel Parra, David Joedicke, J. Manuel Velasco, Gabriel Kronberger, J. Ignacio Hidalgo
• Abstract summary: Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Traditional methods, such as artificial neural networks, have shown high accuracy rates. We propose a novel glucose prediction method emphasising interpretability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose regulation requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, such as artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. In this study, we propose a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the twohour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency.

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