Clinical Medicine Research

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Intelligent Classification Models for Gestational Diabetes: Comparative Study

Received: Apr. 05, 2017    Accepted: Oct. 08, 2017    Published: Dec. 07, 2017
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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.

DOI 10.11648/j.cmr.20170606.14
Published in Clinical Medicine Research ( Volume 6, Issue 6, November 2017 )
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), 2024. Published by Science Publishing Group

Keywords

Diabetes, Gestational, Fuzzy, Classifiers, Diab Care, Mellitus, Memetic Algorithm

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Cite This Article
  • 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

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    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

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    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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  • @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://download.sciencepg.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}
    }
    

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  • 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
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    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
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    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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Author Information
  • Department of Computer Education, Federal College of Education Technical, Lagos, Nigeria

  • Department of Computer Education, Federal College of Education Technical, Lagos, Nigeria

  • Department of Computer Education, Federal College of Education Technical, Lagos, Nigeria

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