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.