Intelligent Classification Models for Gestational Diabetes: Comparative Study
Clinical Medicine Research
Volume 6, Issue 6, November 2017, Pages: 192-200
Received: Apr. 5, 2017; Accepted: Oct. 8, 2017; Published: Dec. 7, 2017
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Authors
Eboka Andrew Okonji, Department of Computer Education, Federal College of Education Technical, Lagos, Nigeria
Okobah Ifeoma Patricia, Department of Computer Education, Federal College of Education Technical, Lagos, Nigeria
Oluwatoyin Yerokun Mary, Department of Computer Education, Federal College of Education Technical, Lagos, Nigeria
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Abstract
Diabetes mellitus, a metabolic disease that features high glucose levels in the body with the inability of the body to secret enough insulin to breakdown glucose, or such a body is resistant to the effects of insulin. Nigeria and other nations of the world have become aware of the inherent threats to life of gestational diabetes in mothers with or without previous cases and its tendencies to metamorphose into Type-II. Our study presents a comparative study of classification models using both the supervised (K-nearest neighborhood and Quadratic Discriminant Analysis) and unsupervised (Profile Hidden Markov Model and Memetic algorithm) methods – which aims at early detection as well as improve early diagnosis via data-mining tools. Adopted dataset is split into: training (in some cases, retraining) and testing to aid model validation. Results show that age, obesity and family ties to the second degree, environmental conditions of inhabitance are critical factors that can increase likelihood. Gestational diabetes in mothers with or without previous cases were confirmed if: (a) history of babies weighing > 4.5kg at birth, (b) insulin resistance with polycystic ovary syndrome, and (c) abnormal tolerance to insulin. Also, PHMM outperforms Memetic algorithm in some cases; while memetic algorithm outperforms PHMM in some cases.
Keywords
Diabetes, Gestational, Fuzzy, Classifiers, Diab Care, Mellitus, Memetic Algorithm
To cite this article
Eboka Andrew Okonji, Okobah Ifeoma Patricia, Oluwatoyin Yerokun Mary, Intelligent Classification Models for Gestational Diabetes: Comparative Study, Clinical Medicine Research. Vol. 6, No. 6, 2017, pp. 192-200. doi: 10.11648/j.cmr.20170606.14
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Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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