Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Automatic blood glucose classification for gestational diabetes with feature selection: Decision trees vs Neural networks
Year:2013
Research Areas
  • Biomedicine,
  • Electronic technology and of the communications
Information
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%.
International
Si
Congress
XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013
960
Place
Sevilla, España
Reviewers
Si
ISBN/ISSN
978-3-319-00846-2
Start Date
25/09/2013
End Date
28/09/2013
From page
1370
To page
1371
IFMBE Proceedings Vol. 41
Participants
  • Autor: Enrique Javier Gomez Aguilera (UPM)
  • Autor: Maria Elena Hernando Perez (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Tecnología Fotónica y Bioingeniería
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