Diabetes therapy prognosis through data stream mining methods and techniques

Dana Wang; Simon Fong; Seoungjae Cho; Kyungeun Cho; Yong Woon Park

doi:10.2316/P.2016.832-066

Diabetes therapy prognosis through data stream mining methods and techniques

Dana Wang, Simon Fong, Seoungjae Cho, Kyungeun Cho, Yong Woon Park

Department of Computer Science and Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016
Publisher	Acta Press
Pages	127-132
Number of pages	6
ISBN (Electronic)	9780889869813
DOIs	https://doi.org/10.2316/P.2016.832-066
State	Published - 2016
Event	12th IASTED International Conference on Biomedical Engineering, BioMed 2016 - Innsbruck, Austria Duration: 15 Feb 2016 → 16 Feb 2016

Publication series

Name	Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016

Conference

Conference	12th IASTED International Conference on Biomedical Engineering, BioMed 2016
Country/Territory	Austria
City	Innsbruck
Period	15/02/16 → 16/02/16

Keywords

Classification algorithms
Data stream mining
Diabetes therapy
Insulin mellitus

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2316/P.2016.832-066

Cite this

Wang, D., Fong, S., Cho, S., Cho, K., & Park, Y. W. (2016). Diabetes therapy prognosis through data stream mining methods and techniques. In Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016 (pp. 127-132). (Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016). Acta Press. https://doi.org/10.2316/P.2016.832-066

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title = "Diabetes therapy prognosis through data stream mining methods and techniques",

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.",

keywords = "Classification algorithms, Data stream mining, Diabetes therapy, Insulin mellitus",

author = "Dana Wang and Simon Fong and Seoungjae Cho and Kyungeun Cho and Park, {Yong Woon}",

year = "2016",

doi = "10.2316/P.2016.832-066",

language = "English",

series = "Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016",

publisher = "Acta Press",

pages = "127--132",

booktitle = "Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016",

address = "Canada",

note = "12th IASTED International Conference on Biomedical Engineering, BioMed 2016 ; Conference date: 15-02-2016 Through 16-02-2016",

}

Wang, D, Fong, S, Cho, S, Cho, K & Park, YW 2016, Diabetes therapy prognosis through data stream mining methods and techniques. in Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016. Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016, Acta Press, pp. 127-132, 12th IASTED International Conference on Biomedical Engineering, BioMed 2016, Innsbruck, Austria, 15/02/16. https://doi.org/10.2316/P.2016.832-066

Diabetes therapy prognosis through data stream mining methods and techniques. / Wang, Dana; Fong, Simon; Cho, Seoungjae et al.
Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016. Acta Press, 2016. p. 127-132 (Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Diabetes therapy prognosis through data stream mining methods and techniques

AU - Wang, Dana

AU - Fong, Simon

AU - Cho, Seoungjae

AU - Cho, Kyungeun

AU - Park, Yong Woon

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Classification algorithms

KW - Data stream mining

KW - Diabetes therapy

KW - Insulin mellitus

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U2 - 10.2316/P.2016.832-066

DO - 10.2316/P.2016.832-066

M3 - Conference contribution

AN - SCOPUS:85015455760

T3 - Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016

SP - 127

EP - 132

BT - Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016

PB - Acta Press

T2 - 12th IASTED International Conference on Biomedical Engineering, BioMed 2016

Y2 - 15 February 2016 through 16 February 2016

ER -

Wang D, Fong S, Cho S, Cho K, Park YW. Diabetes therapy prognosis through data stream mining methods and techniques. In Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016. Acta Press. 2016. p. 127-132. (Proceedings of the 12th IASTED International Conference on Biomedical Engineering, BioMed 2016). doi: 10.2316/P.2016.832-066

Diabetes therapy prognosis through data stream mining methods and techniques

Abstract

Publication series

Conference

Keywords

UN SDGs

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