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Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy
2020
Diagnostics
In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the machine learning models. Then, paired data and corresponding errors are divided scientifically into six different clusters based on blood glucose ranges as per the patient's diabetic conditions. Each cluster is trained to build the unique error
doi:10.3390/diagnostics10050285
pmid:32392841
pmcid:PMC7278000
fatcat:rce4q4ieavbsbjtki2s3bl6jua