The impact of missing continuous blood glucose samples on machine learning models for predicting postprandial hypoglycemia: an experimental analysis

HIGHLIGHTS

• What: This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia which is crucial for diabetes management. The research shows that missing samples generally reduce the model performance but random forest is more robust to missing samples. The approach used in this study had a prediction horizon (PH) of 4 h after each meal. The authors aimed to predict postprandial hypoglycemia using ML-based systems, particularly examining the impact of missing CGM data on the predictive performance of these models.
• Who: Najib Ur Rehman and colleagues from . . .
  

   

  

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