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Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models

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Abstract

Glucose concentration in type 1 diabetes is a function of biological and environmental factors which present high inter-patient variability. The objective of this study is to evaluate a number of features, which are extracted from medical and lifestyle self-monitoring data, with respect to their ability to predict the short-term subcutaneous (s.c.) glucose concentration of an individual. Random forests (RF) and RReliefF algorithms are first employed to rank the candidate feature set. Then, a forward selection procedure follows to build a glucose predictive model, where features are sequentially added to it in decreasing order of importance. Predictions are performed using support vector regression or Gaussian processes. The proposed method is validated on a dataset of 15 type diabetics in real-life conditions. The s.c. glucose profile along with time of the day and plasma insulin concentration are systematically highly ranked, while the effect of food intake and physical activity varies considerably among patients. Moreover, the average prediction error converges in less than d/2 iterations (d is the number of features). Our results suggest that RF and RReliefF can find the most informative features and can be successfully used to customize the input of glucose models.

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Acknowledgments

This work is supported by the research project “Development of an Information Environment for Diabetes Data Analysis and New Knowledge Mining” that has been co-financed by the European Union [European Regional Development Fund (ERDF)] and Greek national funds through the Operational Program “THESSALY-MAINLAND GREECE AND EPIRUS-2007-2013” of the National Strategic Reference Framework (NSRF 2007–2013).

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Authors and Affiliations

  1. Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110, Ioannina, Greece

    Eleni I. Georga, Vasilios C. Protopappas & Dimitrios I. Fotiadis

  2. Department of Mechanical Engineering and Aeronautics, University of Patras, 26500, Patras, Greece

    Demosthenes Polyzos

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  1. Eleni I. Georga
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  2. Vasilios C. Protopappas
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  4. Dimitrios I. Fotiadis
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Correspondence to Dimitrios I. Fotiadis.

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Georga, E.I., Protopappas, V.C., Polyzos, D. et al. Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models. Med Biol Eng Comput 53, 1305–1318 (2015). https://doi.org/10.1007/s11517-015-1263-1

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