A new multivariate blood glucose prediction method with hybrid feature clustering and online transfer learning

Accurate blood glucose (BG) prediction is greatly benefit for the treatment of diabetes. Generally, clinical physicians are required to comprehensively analyze various factors, such as patient's body temperature, meal, sleep, insulin injection, continuous glucose monitoring (CGM), and other information, to evaluate the fluctuation trend of blood glucose. To address this problem, this paper proposes a multivariate blood glucose prediction method based on mixed feature clustering. It clusters time series data with diverse or mixed features related to blood glucose, effectively leveraging correlations and distribution characteristics. By combining incremental clustering of multivariate time series with transfer learning, this method achieves online prediction of blood glucose levels. The experimental results indicate that the proposed method can decrease the prediction error RMSE by 4.2% (PH=30min) and 5.9% (PH=60min). Compared with other prediction methods, the training time of the multivariate prediction method is reduced by 5.2% (PH=30min) and 4.7% (PH=60min). It was also validated and compared with other methods in a real dataset. The proposed method in this study has lower prediction error and better prediction performance in the prediction horizon (PH) of PH=30, 45, 60, 75, and 90 min, respectively. Compared with the traditional unitary and multivariate time series prediction method, the approach proposed in this paper significantly improves the accuracy and robustness of blood glucose prediction. According to the evaluation results on the data set from OhioT1DM and the Sixth People's Hospital of Shanghai, the proposed method has better generalization performance and clinical acceptability.