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dc.contributor.authorChan, Kit Yan
dc.contributor.authorLing, S.
dc.contributor.authorDillon, Tharam
dc.contributor.authorNguyen, H.
dc.identifier.citationChan, K.Y. and Ling, S.H. and Dillon, T.S. and Nguyen, H.T. 2011. Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. Expert Systems with Applications. 38 (8): pp. 9799-9808.

Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients’ data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients’ data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients.

dc.subjecthypoglycemic episodes
dc.subjectgenetic algorithm
dc.subjectNeural networks
dc.subjecttype 1 diabetes mellitus
dc.subjectmedical diagnosis
dc.titleDiagnosis of hypoglycemic episodes using a neural network based rule discovery system
dc.typeJournal Article
dcterms.source.titleExpert Systems with Applications

NOTICE: this is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, Vol.38, no.8 (August 2011). DOI: 10.1016/j.eswa.2011.02.020

curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access

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