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Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus

Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus

Saeed Rouhani, Maryam MirSharif
Copyright: © 2018 |Volume: 8 |Issue: 1 |Pages: 11
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781522544661|DOI: 10.4018/IJKDB.2018010101
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MLA

Rouhani, Saeed, and Maryam MirSharif. "Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus." IJKDB vol.8, no.1 2018: pp.1-11. http://doi.org/10.4018/IJKDB.2018010101

APA

Rouhani, S. & Maryam MirSharif. (2018). Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 1-11. http://doi.org/10.4018/IJKDB.2018010101

Chicago

Rouhani, Saeed, and Maryam MirSharif. "Data Mining Approach for the Early Risk Assessment of Gestational Diabetes Mellitus," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 1-11. http://doi.org/10.4018/IJKDB.2018010101

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Abstract

In this article, the authors proposed the method of medical diagnosis in gestational diabetes mellitus (GDM) in the initial stages of pregnancy to facilitate diagnoses and prevent the affection. Nowadays, in industrial modern world with changing lifestyle alimental manner the incidence of complex disease has been increasingly grown. GDM is a chronic disease and one of the major health problems that is often diagnosed in middle or late period of pregnancy, when it is too late for prediction. If it is not treated, it will make serious complications and various side effects for mother and child. This article is designed for answering to the question of: “What is the best approach in timely and accurate prediction of GDM?” Thus, the artificial neural network and decision tree are proposed to reduce the amount of error and the level of accuracy in anticipating and improving the precision of prediction. The results illustrate that intelligent diagnosis systems can improve the quality of healthcare, timely prediction, prevention, and knowledge discovery in bioinformatics.

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