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.
| Published in | Clinical Medicine Research (Volume 6, Issue 6) |
| DOI | 10.11648/j.cmr.20170606.14 |
| Page(s) | 192-200 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Diabetes, Gestational, Fuzzy, Classifiers, Diab Care, Mellitus, Memetic Algorithm
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APA Style
Eboka Andrew Okonji, Okobah Ifeoma Patricia, Oluwatoyin Yerokun Mary. (2017). Intelligent Classification Models for Gestational Diabetes: Comparative Study. Clinical Medicine Research, 6(6), 192-200. https://doi.org/10.11648/j.cmr.20170606.14
ACS Style
Eboka Andrew Okonji; Okobah Ifeoma Patricia; Oluwatoyin Yerokun Mary. Intelligent Classification Models for Gestational Diabetes: Comparative Study. Clin. Med. Res. 2017, 6(6), 192-200. doi: 10.11648/j.cmr.20170606.14
AMA Style
Eboka Andrew Okonji, Okobah Ifeoma Patricia, Oluwatoyin Yerokun Mary. Intelligent Classification Models for Gestational Diabetes: Comparative Study. Clin Med Res. 2017;6(6):192-200. doi: 10.11648/j.cmr.20170606.14
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Download@article{10.11648/j.cmr.20170606.14,
author = {Eboka Andrew Okonji and Okobah Ifeoma Patricia and Oluwatoyin Yerokun Mary},
title = {Intelligent Classification Models for Gestational Diabetes: Comparative Study},
journal = {Clinical Medicine Research},
volume = {6},
number = {6},
pages = {192-200},
doi = {10.11648/j.cmr.20170606.14},
url = {https://doi.org/10.11648/j.cmr.20170606.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cmr.20170606.14},
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.},
year = {2017}
}
TY - JOUR T1 - Intelligent Classification Models for Gestational Diabetes: Comparative Study AU - Eboka Andrew Okonji AU - Okobah Ifeoma Patricia AU - Oluwatoyin Yerokun Mary Y1 - 2017/12/07 PY - 2017 N1 - https://doi.org/10.11648/j.cmr.20170606.14 DO - 10.11648/j.cmr.20170606.14 T2 - Clinical Medicine Research JF - Clinical Medicine Research JO - Clinical Medicine Research SP - 192 EP - 200 PB - Science Publishing Group SN - 2326-9057 UR - https://doi.org/10.11648/j.cmr.20170606.14 AB - 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. VL - 6 IS - 6 ER -
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