Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

Azizur Rahman; Karimon Nesha; Mariam Akter; Md. Sheikh Giash Uddin

doi:doi:10.11648/j.sjph.20130101.16

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Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status

Azizur Rahman, Karimon Nesha, Mariam Akter, Md. Sheikh Giash Uddin

Published in Science Journal of Public Health (Volume 1, Issue 1)

Published: 10 March 2013

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Abstract

Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enth-ropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.

Published in	Science Journal of Public Health (Volume 1, Issue 1)
DOI	10.11648/j.sjph.20130101.16
Page(s)	39-43
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), 2013. Published by Science Publishing Group

Keywords

Artificial Neural Network (ANN), Binary Logistic (LR), Classification, Diabetes Mellitus (DM)

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APA Style

Azizur Rahman, Karimon Nesha, Mariam Akter, Md. Sheikh Giash Uddin. (2013). Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status. Science Journal of Public Health, 1(1), 39-43. https://doi.org/10.11648/j.sjph.20130101.16

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ACS Style

Azizur Rahman; Karimon Nesha; Mariam Akter; Md. Sheikh Giash Uddin. Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status. Sci. J. Public Health 2013, 1(1), 39-43. doi: 10.11648/j.sjph.20130101.16

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AMA Style

Azizur Rahman, Karimon Nesha, Mariam Akter, Md. Sheikh Giash Uddin. Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status. Sci J Public Health. 2013;1(1):39-43. doi: 10.11648/j.sjph.20130101.16

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@article{10.11648/j.sjph.20130101.16,
  author = {Azizur Rahman and Karimon Nesha and Mariam Akter and Md. Sheikh Giash Uddin},
  title = {Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status},
  journal = {Science Journal of Public Health},
  volume = {1},
  number = {1},
  pages = {39-43},
  doi = {10.11648/j.sjph.20130101.16},
  url = {https://doi.org/10.11648/j.sjph.20130101.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjph.20130101.16},
  abstract = {Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enth-ropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.},
 year = {2013}
}

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TY  - JOUR
T1  - Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status
AU  - Azizur Rahman
AU  - Karimon Nesha
AU  - Mariam Akter
AU  - Md. Sheikh Giash Uddin
Y1  - 2013/03/10
PY  - 2013
N1  - https://doi.org/10.11648/j.sjph.20130101.16
DO  - 10.11648/j.sjph.20130101.16
T2  - Science Journal of Public Health
JF  - Science Journal of Public Health
JO  - Science Journal of Public Health
SP  - 39
EP  - 43
PB  - Science Publishing Group
SN  - 2328-7950
UR  - https://doi.org/10.11648/j.sjph.20130101.16
AB  - Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enth-ropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.
VL  - 1
IS  - 1
ER  -

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Author Information

Azizur Rahman

Department of Statistics, Jagannath University, Dhaka-1100, Bangladesh
Karimon Nesha

Department of Disaster management, University of Dhaka, 2School of Business, United International
Mariam Akter

Department of Disaster management, University of Dhaka, 2School of Business, United International
Md. Sheikh Giash Uddin

Department of Statistics, Jagannath University, Dhaka-1100, Bangladesh

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APA Style

Azizur Rahman, Karimon Nesha, Mariam Akter, Md. Sheikh Giash Uddin. (2013). Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status. Science Journal of Public Health, 1(1), 39-43. https://doi.org/10.11648/j.sjph.20130101.16

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ACS Style

Azizur Rahman; Karimon Nesha; Mariam Akter; Md. Sheikh Giash Uddin. Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status. Sci. J. Public Health 2013, 1(1), 39-43. doi: 10.11648/j.sjph.20130101.16

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AMA Style

Azizur Rahman, Karimon Nesha, Mariam Akter, Md. Sheikh Giash Uddin. Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status. Sci J Public Health. 2013;1(1):39-43. doi: 10.11648/j.sjph.20130101.16

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@article{10.11648/j.sjph.20130101.16,
  author = {Azizur Rahman and Karimon Nesha and Mariam Akter and Md. Sheikh Giash Uddin},
  title = {Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status},
  journal = {Science Journal of Public Health},
  volume = {1},
  number = {1},
  pages = {39-43},
  doi = {10.11648/j.sjph.20130101.16},
  url = {https://doi.org/10.11648/j.sjph.20130101.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjph.20130101.16},
  abstract = {Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enth-ropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.},
 year = {2013}
}

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TY  - JOUR
T1  - Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status
AU  - Azizur Rahman
AU  - Karimon Nesha
AU  - Mariam Akter
AU  - Md. Sheikh Giash Uddin
Y1  - 2013/03/10
PY  - 2013
N1  - https://doi.org/10.11648/j.sjph.20130101.16
DO  - 10.11648/j.sjph.20130101.16
T2  - Science Journal of Public Health
JF  - Science Journal of Public Health
JO  - Science Journal of Public Health
SP  - 39
EP  - 43
PB  - Science Publishing Group
SN  - 2328-7950
UR  - https://doi.org/10.11648/j.sjph.20130101.16
AB  - Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to compare and explore the process of constructing common predictive models. Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were applied and compared in their ability to classifying disease-free subjects and those with diabetes mellitus(DM) diagnosed by glucose level. Demographic, enth-ropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460 participants 133 (28.91%) suffered from DM, 116 (25.27%) had impaired glucose tolerance (IGT) and the remainder 211 (45.86%) were disease free. Among other factors, family history of diabetes and arsenic exposure were found as significant risk factors for developing diabetes mellitus (DM), with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 73.79% of cases with IGT or DM in the training datasets, 70.96% in testing datasets and 70.4% of all subjects. On the other hand, the sensitivities of artificial neural network architecture for training and testing datasets and for all subjects were 83.4%, 82.25% and 84.33% respectively, indicate better performance than binary logistic regression model.
VL  - 1
IS  - 1
ER  -

Copy | Download