BLOD GLUCOSE DATA SET OPTIMIZATION FOR IMPROVED HYPOGLYCEMIA PREDICTION BASED ON MACHINE LEARNING IMPLEMENTATION INGESTION

The invention relates to a method for data set expansion for improved hypoglycaemia prediction based on classifier ingestion, and comprises the steps of: providing a raw data set for a subject, the data set comprising a plurality of BG values obtained at a given sampling rate and thereto associated time stamps over a plurality of days N, and performing data transformation by rolling scheme temporal binning of evaluation block values (eHH) as input X to create corresponding prediction values (pHH) as output Y, wherein X is created as a sliding window comprising BG values for a given past period of time T−p, and wherein Y is created as an indicator I indicating whether or not a BG value at a given future time T−f is below a given threshold indicative of a hypoglycaemic condition.