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Diabetes Therapy Prognosis through Data Stream Mining Methods and Techniques
Dana Wang2; Simon Fong2; Seoungjae Cho3; Kyungeun Cho3; Yongwoon Park1
2016
Conference NameBiomedical Engineering (BioMed 2016)
Source PublicationProceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016
Pages127-132
Conference DateFebruary 15 – 16, 2016
Conference PlaceInnsbruck, Austria
Abstract

Diabetes is one of the frequently occurring non-communicable diseases that lead causes of deaths among the worldwide. Maintain an appropriate blood glucose value for the patient needs a right amount of insulin dosage and the timing of its intake. But the medical interaction to the different lifestyle patients cause to the complexity of the therapy. In this article, a real-time classification therapy prognosis model is proposed to compute for regulating IDDM based on the daily prescription record and patients' individual blood glucose pattern by using data stream mining. A computer simulation is presented for evaluating the most appropriate data stream algorithms for this task.

KeywordDiabetes Therapy Insulin Mellitus Classification Algorithms Data Stream Mining
DOI10.2316/P.2016.832-066
URLView the original
Language英語English
Scopus ID2-s2.0-85015455760
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Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Korean Agency for Defense Development
2.Universidade de Macau
3.Dongguk University, Seoul
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Dana Wang,Simon Fong,Seoungjae Cho,et al. Diabetes Therapy Prognosis through Data Stream Mining Methods and Techniques[C], 2016, 127-132.
APA Dana Wang., Simon Fong., Seoungjae Cho., Kyungeun Cho., & Yongwoon Park (2016). Diabetes Therapy Prognosis through Data Stream Mining Methods and Techniques. Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016, 127-132.
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