Hypoglycemia detection using fuzzy inference system with genetic algorithm

Sai Ho Ling, Hung T. Nguyen, Hung Fat Frank Leung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


In this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the electrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.
Original languageEnglish
Title of host publicationFUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Number of pages7
Publication statusPublished - 27 Sept 2011
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
Duration: 27 Jun 201130 Jun 2011


Conference2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011


  • Diabetes
  • Fuzzy logic
  • Genetic algorithm
  • Hypoglycemia

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Hypoglycemia detection using fuzzy inference system with genetic algorithm'. Together they form a unique fingerprint.

Cite this