Title
Prediction of personalized blood glucose levels in type 1 diabetic patients using a neuroevolution approach
Abstract
ABSTRACTDiabetes mellitus is a lifelong disease in which either the pancreas fails to produce insulin or the produced amount is insufficient to control blood sugar levels. A way to tackle this malfunctioning is to devise an artificial pancreas endowed with a personalized control algorithm able to regulate the insulin dosage. A crucial step in realizing such a device is to effectively forecast future glucose levels starting from past glucose values, the knowledge of the food intake, and of the basal and the injected insulin. The increasing availability of medical diabetes data sets is providing unprecedented opportunities to identify correlations inside these data even harnessing innovative investigation methods, such as deep learning. As an alternative to the deep learning methods successfully used as forecasting models, we exploit a neuroevolution algorithm to model and predict future personalized blood glucose levels. The discovered subjective regression model can represent the control algorithm of an artificial pancreas. This model is assessed through experiments performed on a real-world database containing data of six patients suffering from Type 1 diabetes. To further evaluate the effectiveness of the predictions derived from the proposed approach, the results are also compared against those obtained by other state-of-the-art recently proposed methods.
Year
DOI
Venue
2021
10.1145/3449726.3463143
Genetic and Evolutionary Computation Conference
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Ivanoe De Falco124234.58
Antonio Della Cioppa214120.70
Angelo Marcelli313932.42
Umberto Scafuri411616.33
Luca Stellaccio500.34
Ernesto Tarantino636142.45