BGformer: An improved Informer model to enhance blood glucose prediction

Abstract

Accurately predicting blood glucose levels is crucial in diabetes management to mitigate patients’ risk of complications. However, blood glucose values exhibit instability, and existing prediction methods often struggle to capture their volatile nature, leading to inaccurate trend forecasts. To address these challenges, we propose a novel blood glucose level prediction model based on the Informer architecture: BGformer. Our model introduces a feature enhancement module and a microscale overlapping concerns mechanism. The feature enhancement module integrates periodic and trend feature extractors, enhancing the model’s ability to capture relevant information from the data. By extending the feature extraction capacity of time series data, it provides richer feature representations for analysis. Meanwhile, the microscale overlapping concerns mechanism adopts a window-based strategy, computing attention scores only within specific windows. This approach reduces computational complexity while enhancing the model’s capacity to capture local temporal dependencies. Furthermore, we introduce a dual attention enhancement module to augment the model’s expressive capability. Through prediction experiments on blood glucose values from sixteen diabetic patients, our model outperformed eight benchmark models in terms of both MAE and RMSE metrics for future 60-minute and 90-minute predictions. Our proposed scheme significantly improves the model’s dependency-capturing ability, resulting in more accurate blood glucose level predictions.