PREDICTION OF BLOOD GLUCOSE LEVEL OSCILLATIONS IN TYPE1 DIABETES BY USING ELMAN RECURRENT NEURAL NETWORK

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

RAHATI QUCHANI S.; TAHAMI S.E.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF TECHNICAL-ENGINEERING Year:2008 | Volume:2 | Issue:1 Page(s): 81-91

Download Full-Text

Persian Verion

View:

7,217

Download:

Cites:

Information Journal Paper

Title

PREDICTION OF BLOOD GLUCOSE LEVEL OSCILLATIONS IN TYPE1 DIABETES BY USING ELMAN RECURRENT NEURAL NETWORK

Author(s)

RAHATI QUCHANI S. | TAHAMI S.E. | Issue Writer Certificate

Keywords

DIABETESQ3

BLOOD GLUCOSE PREDICTIONQ3

MLP NEURAL NETWORKQ3

RECURRENT NEURAL NETWORKQ2

Abstract

One of the most dangerous symptoms of Type 1 DIABETES is the frequent and great oscillation of blood glucose level that can lead the patient to unconscious and coma states. So being able to predict and finally prevent these symptoms would help the diabetic patients. This paper attempts to use Elman neural networks to predict the blood glucose levels in type1 diabetic patients. Data set used in this paper consists of the protocol of 3 Iranian type1 Diabetic women and includes parameters such as type and dosage of injected insulin, the period of time (in hour) between two consecutive measurements of the blood glucose level, carbohydrate intake, exercise and the blood glucose level measured at start of the given period of time. Finally we concluded that the use of RECURRENT NEURAL NETWORK such as Elman with great reduction of prediction error and also reduction of number of layers and neurons used in the construction of Neural Networks as compared with other method can be an appropriate model to predict the long term blood glucose levels in type 1 DIABETES.

Cites

پیش بینی صحیح دز «انسولین و گلیبن کلامید» در بیماران دیابتی مبتنی بر ترکیب سیستم های هوشمند و سابقه بیماری

References

No record.

Cite

APA: Copy

RAHATI QUCHANI, S., & TAHAMI, S.E.. (2008). PREDICTION OF BLOOD GLUCOSE LEVEL OSCILLATIONS IN TYPE1 DIABETES BY USING ELMAN RECURRENT NEURAL NETWORK. JOURNAL OF TECHNICAL-ENGINEERING, 2(1), 81-91. SID. https://sid.ir/paper/193957/en

Vancouver: Copy

RAHATI QUCHANI S., TAHAMI S.E.. PREDICTION OF BLOOD GLUCOSE LEVEL OSCILLATIONS IN TYPE1 DIABETES BY USING ELMAN RECURRENT NEURAL NETWORK. JOURNAL OF TECHNICAL-ENGINEERING[Internet]. 2008;2(1):81-91. Available from: https://sid.ir/paper/193957/en

IEEE: Copy

S. RAHATI QUCHANI, and S.E. TAHAMI, “PREDICTION OF BLOOD GLUCOSE LEVEL OSCILLATIONS IN TYPE1 DIABETES BY USING ELMAN RECURRENT NEURAL NETWORK,” JOURNAL OF TECHNICAL-ENGINEERING, vol. 2, no. 1, pp. 81–91, 2008, [Online]. Available: https://sid.ir/paper/193957/en

Related Journal Papers