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Automatic Blood Glucose Classification for Gestational Diabetes with Feature Selection: Decision Trees vs. Neural Networks

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Part of the IFMBE Proceedings book series (IFMBE,volume 41)

Abstract

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

Keywords

  • Classification
  • decision support
  • diabetes
  • decision trees
  • neural networks

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© 2014 Springer International Publishing Switzerland

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Caballero-Ruiz, E. et al. (2014). Automatic Blood Glucose Classification for Gestational Diabetes with Feature Selection: Decision Trees vs. Neural Networks. In: Roa Romero, L. (eds) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. IFMBE Proceedings, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-00846-2_339

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  • DOI: https://doi.org/10.1007/978-3-319-00846-2_339

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00845-5

  • Online ISBN: 978-3-319-00846-2

  • eBook Packages: EngineeringEngineering (R0)