Multilayer perceptron application for diabetes mellitus prediction in pregnancy care

Mário W.L. Moreira; Joel J.P.C. Rodrigues; Neeraj Kumar; Jianwei Niu; Arun Kumar Sangaiah

doi:10.1007/978-981-10-7398-4_22

Multilayer perceptron application for diabetes mellitus prediction in pregnancy care

Mário W.L. Moreira
, Joel J.P.C. Rodrigues^*
, Neeraj Kumar
, Jianwei Niu
, Arun Kumar Sangaiah

^*Corresponding author for this work

School of Computer Science and Engineering

University of Beira Interior
Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Fortaleza
Instituto Nacional de Telecomunicações
Universidade de Fortaleza
Thapar Institute of Engineering & Technology
Vellore Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The human intelligence modeling by brain components simulation, such as neurons and their connections, is part of leading smart decision computing paradigms. In Health, artificial neural networks (ANN) have the capacity to adapt to uncertainty situations and learn even with inaccurate data. This paper presents the modeling and performance evaluation of an ANN-based technique, named multilayer perceptron (MLP), for gestational diabetes mellitus (GDM) prediction that is responsible for several severe complications and affects 3 to 7% of pregnancies worldwide. Results show that this approach reached a precision of 0.74, Recall 0.741, F-measure 0.741, and ROC area 0.779. These indicators show that this method is an excellent predictor of this disease. This contribution offers a computational intelligence (CI) tool capable of identifying risk cases during pregnancy and, thus, reduce possible sequels for both pregnant woman and fetus.

Original language	English
Title of host publication	Frontier Computing - Theory, Technologies and Applications FC 2017
Editors	Neil Y. Yen, Jason C. Hung, Lin Hui
Publisher	Springer Verlag
Pages	200-209
Number of pages	10
ISBN (Print)	9789811073977
DOIs	https://doi.org/10.1007/978-981-10-7398-4_22
State	Published - 2018
Event	6th International Conference on Frontier Computing, FC 2017 - Osaka, Japan Duration: 12 Jul 2017 → 14 Jul 2017

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	464
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	6th International Conference on Frontier Computing, FC 2017
Country/Territory	Japan
City	Osaka
Period	12/07/17 → 14/07/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial neural networks
Gestational diabetes mellitus
Intelligent decision computing
Multilayer perceptron
Pregnancy

Access to Document

10.1007/978-981-10-7398-4_22

Cite this

Moreira, M. W. L., Rodrigues, J. J. P. C., Kumar, N., Niu, J., & Sangaiah, A. K. (2018). Multilayer perceptron application for diabetes mellitus prediction in pregnancy care. In N. Y. Yen, J. C. Hung, & L. Hui (Eds.), Frontier Computing - Theory, Technologies and Applications FC 2017 (pp. 200-209). (Lecture Notes in Electrical Engineering; Vol. 464). Springer Verlag. https://doi.org/10.1007/978-981-10-7398-4_22

@inproceedings{04619b81549945798d762765c764d056,

title = "Multilayer perceptron application for diabetes mellitus prediction in pregnancy care",

abstract = "The human intelligence modeling by brain components simulation, such as neurons and their connections, is part of leading smart decision computing paradigms. In Health, artificial neural networks (ANN) have the capacity to adapt to uncertainty situations and learn even with inaccurate data. This paper presents the modeling and performance evaluation of an ANN-based technique, named multilayer perceptron (MLP), for gestational diabetes mellitus (GDM) prediction that is responsible for several severe complications and affects 3 to 7\% of pregnancies worldwide. Results show that this approach reached a precision of 0.74, Recall 0.741, F-measure 0.741, and ROC area 0.779. These indicators show that this method is an excellent predictor of this disease. This contribution offers a computational intelligence (CI) tool capable of identifying risk cases during pregnancy and, thus, reduce possible sequels for both pregnant woman and fetus.",

keywords = "Artificial neural networks, Gestational diabetes mellitus, Intelligent decision computing, Multilayer perceptron, Pregnancy",

author = "Moreira, \{M{\'a}rio W.L.\} and Rodrigues, \{Joel J.P.C.\} and Neeraj Kumar and Jianwei Niu and Sangaiah, \{Arun Kumar\}",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; 6th International Conference on Frontier Computing, FC 2017 ; Conference date: 12-07-2017 Through 14-07-2017",

year = "2018",

doi = "10.1007/978-981-10-7398-4\_22",

language = "英语",

isbn = "9789811073977",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "200--209",

editor = "Yen, \{Neil Y.\} and Hung, \{Jason C.\} and Lin Hui",

booktitle = "Frontier Computing - Theory, Technologies and Applications FC 2017",

address = "德国",

}

Moreira, MWL, Rodrigues, JJPC, Kumar, N, Niu, J & Sangaiah, AK 2018, Multilayer perceptron application for diabetes mellitus prediction in pregnancy care. in NY Yen, JC Hung & L Hui (eds), Frontier Computing - Theory, Technologies and Applications FC 2017. Lecture Notes in Electrical Engineering, vol. 464, Springer Verlag, pp. 200-209, 6th International Conference on Frontier Computing, FC 2017, Osaka, Japan, 12/07/17. https://doi.org/10.1007/978-981-10-7398-4_22

Multilayer perceptron application for diabetes mellitus prediction in pregnancy care. / Moreira, Mário W.L.; Rodrigues, Joel J.P.C.; Kumar, Neeraj et al.
Frontier Computing - Theory, Technologies and Applications FC 2017. ed. / Neil Y. Yen; Jason C. Hung; Lin Hui. Springer Verlag, 2018. p. 200-209 (Lecture Notes in Electrical Engineering; Vol. 464).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multilayer perceptron application for diabetes mellitus prediction in pregnancy care

AU - Moreira, Mário W.L.

AU - Rodrigues, Joel J.P.C.

AU - Kumar, Neeraj

AU - Niu, Jianwei

AU - Sangaiah, Arun Kumar

PY - 2018

Y1 - 2018

N2 - The human intelligence modeling by brain components simulation, such as neurons and their connections, is part of leading smart decision computing paradigms. In Health, artificial neural networks (ANN) have the capacity to adapt to uncertainty situations and learn even with inaccurate data. This paper presents the modeling and performance evaluation of an ANN-based technique, named multilayer perceptron (MLP), for gestational diabetes mellitus (GDM) prediction that is responsible for several severe complications and affects 3 to 7% of pregnancies worldwide. Results show that this approach reached a precision of 0.74, Recall 0.741, F-measure 0.741, and ROC area 0.779. These indicators show that this method is an excellent predictor of this disease. This contribution offers a computational intelligence (CI) tool capable of identifying risk cases during pregnancy and, thus, reduce possible sequels for both pregnant woman and fetus.

AB - The human intelligence modeling by brain components simulation, such as neurons and their connections, is part of leading smart decision computing paradigms. In Health, artificial neural networks (ANN) have the capacity to adapt to uncertainty situations and learn even with inaccurate data. This paper presents the modeling and performance evaluation of an ANN-based technique, named multilayer perceptron (MLP), for gestational diabetes mellitus (GDM) prediction that is responsible for several severe complications and affects 3 to 7% of pregnancies worldwide. Results show that this approach reached a precision of 0.74, Recall 0.741, F-measure 0.741, and ROC area 0.779. These indicators show that this method is an excellent predictor of this disease. This contribution offers a computational intelligence (CI) tool capable of identifying risk cases during pregnancy and, thus, reduce possible sequels for both pregnant woman and fetus.

KW - Artificial neural networks

KW - Gestational diabetes mellitus

KW - Intelligent decision computing

KW - Multilayer perceptron

KW - Pregnancy

UR - https://www.scopus.com/pages/publications/85045997271

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DO - 10.1007/978-981-10-7398-4_22

M3 - 会议稿件

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SN - 9789811073977

T3 - Lecture Notes in Electrical Engineering

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EP - 209

BT - Frontier Computing - Theory, Technologies and Applications FC 2017

A2 - Yen, Neil Y.

A2 - Hung, Jason C.

A2 - Hui, Lin

PB - Springer Verlag

T2 - 6th International Conference on Frontier Computing, FC 2017

Y2 - 12 July 2017 through 14 July 2017

ER -

Multilayer perceptron application for diabetes mellitus prediction in pregnancy care

Abstract

Publication series

Conference

UN SDGs

Keywords

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