Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes

Caballero Ruiz, Estefanía and García Sáez, Gema and Rigla Cros, Mercedes and Villaplana, María and Pons, Belén and Hernando Pérez, María Elena (2016). Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes. "Expert Systems With Applications", v. 63 ; pp. 386-396. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2016.07.019.

Description

Title: Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes
Author/s:
  • Caballero Ruiz, Estefanía
  • García Sáez, Gema
  • Rigla Cros, Mercedes
  • Villaplana, María
  • Pons, Belén
  • Hernando Pérez, María Elena
Item Type: Article
Título de Revista/Publicación: Expert Systems With Applications
Date: 30 November 2016
ISSN: 0957-4174
Volume: 63
Subjects:
Freetext Keywords: Automatic classification Decision support Expert systems Gestational diabetes Machine learning Telemedicine
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica y Bioingeniería
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Expert systems for diabetes care need to automatically evaluate glycaemia measurements in relationship to meals to correctly determine patients' metabolic condition and generate recommendations about therapy adjustments. Most glucose meters allow patients to manually label each measurement with a meal tag, but as this utility is not always used, a completion procedure is needed. Classification methods are usually based on predefined mealtimes and present insufficient accuracy that might affect the automatic data analysis. Expert systems in diabetes require a reliable method to manage incomplete glycaemia data so that they can determine if patients' metabolic condition is altered due to a specific meal or due to an extended fasting period. This paper presents the design and application of a classification module to automatically assign the appropriate meal and 'moment of measurement' to incomplete glycaemia data. Different machine learning techniques were studied in order to design the best classification algorithm in terms of accuracy. The selected classifier was implemented with a C4.5 decision tree with 7 input features selected with a wrapper evaluator and the genetic search algorithm, which achieved 95.45% of accuracy with the training set on cross-validation. The classification module was integrated in the Sinedie expert system for gestational diabetes care and was evaluated in a clinical environment for 8 months with 42 patients. A total of 7,113 glycaemia measurements were uploaded by patients into the Sinedie system and were completed by the 'classification module'. The 98.79% of the measurements were correctly classified, while patients modified the automatic classification of 1.21% of them. Classification results were improved by 21.04% compared to a classification based on predefined mealtimes. The automatic classification of glycaemia measurements minimizes the patient's intervention, allows structuring measurements in relationship to meals and makes automatic data interpretation by expert systems more reliable.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainPII0/01125SinedieUnspecifiedUnspecified

More information

Item ID: 46037
DC Identifier: http://oa.upm.es/46037/
OAI Identifier: oai:oa.upm.es:46037
DOI: 10.1016/j.eswa.2016.07.019
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417416303645
Deposited by: Memoria Investigacion
Deposited on: 28 May 2017 07:36
Last Modified: 30 Nov 2018 23:30
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