Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms

HIGHLIGHTS

• who: Brian Bogue-Jimenez et al. from the Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN, USA have published the research work: Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms, in the Journal: Sensors 2022, 3534 of /2022/
• what: The authors investigated the feasibility of a novel approach to NICGM using multiple off-the-shelf wearable and learning-based models (i.e. machine learning) to predict blood glucose. The authors investigate a synergistic approach to accurately predict BGLs by combining noninvasive . . .
  

   

  

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