Title
Multivariate Prediction of Subcutaneous Glucose Concentration in Type 1 Diabetes Patients Based on Support Vector Regression.
Abstract
Data-driven techniques have recently drawn significant interest in the predictive modeling of subcutaneous (s.c.) glucose concentration in type 1 diabetes. In this study, the s.c. glucose prediction is treated as a multivariate regression problem, which is addressed using support vector regression (SVR). The proposed method is based on variables concerning: 1) the s.c. glucose profile; 2) the plasma insulin concentration; 3) the appearance of meal-derived glucose in the systemic circulation; and 4) the energy expenditure during physical activities. Six cases corresponding to different combinations of the aforementioned variables are used to investigate the influence of the input on the daily glucose prediction. The proposed method is evaluated using a dataset of 27 patients in free-living conditions. Tenfold cross validation is applied to each dataset individually to both optimize and test the SVR model. In the case, where all the input variables are considered, the average prediction errors are 5.21, 6.03, 7.14, and 7.62 mg/dl for 15-, 30-, 60-, and 120-min prediction horizons, respectively. The results clearly indicate that the availability of multivariable data and their effective combination can significantly increase the accuracy of both short-term and long-term predictions.
Year
DOI
Venue
2013
10.1109/TITB.2012.2219876
IEEE J. Biomedical and Health Informatics
Keywords
Field
DocType
biochemistry,regression analysis,support vector machines
Diabetes mellitus,Multivariable calculus,Pattern recognition,Regression analysis,Multivariate statistics,Support vector machine,Artificial intelligence,Insulin,Type 1 diabetes,Cross-validation,Medicine
Journal
Volume
Issue
ISSN
17
1
2168-2194
Citations 
PageRank 
References 
15
0.77
10
Authors
7