Long Term HbA1c Prediction Using Multi-Stage CGM Data Analysis

Abstract

The glycated hemoglobin (HbA1c) is regarded as an essential biomarker for diabetes management. Having an elevated HbA1c level significantly increases the risk of developing diabetes-related health complications. Accurate prediction of HbA1c can greatly improve the way diabetic patients are treated and can potentially avoid related consequences. This study devises a framework to predict HbA1c levels 2-3 months in advance by using blood glucose data collected through continuous glucose monitoring (CGM) sensors and leveraging advanced feature extraction and machine learning techniques. The CGM data may often contain missing values due to sensor issues or not wearing the sensor for some period. Thus, in the paper, a novel missing data estimation method has been proposed for a single data point, multiple data points, and entire day CGM data imputation. The CGM data have been rigorously investigated, and pertinent features were created along with a multi-stage multi-class (MSMC) classification model to predict futuristic HbA1c levels. To evaluate the developed framework, a total of 150 patients' data were sourced from Sidra Medicine, Doha, Qatar, for analysis. The proposed three-staged and five-staged MSMC models predicted HbA1c levels 2-3 months in advance and obtained overall classification accuracies of 88.65% and 83.41%, respectively.