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DOI: 10.14569/IJACSA.2019.0100846
PDF

Classifying Cardiotocography Data based on Rough Neural Network

Author 1: Belal Amin
Author 2: Mona Gamal
Author 3: A. A. Salama
Author 4: I.M. El-Henawy
Author 5: Khaled Mahfouz

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 8, 2019.

  • Abstract and Keywords
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Abstract: Cardiotocography is a medical device that monitors fetal heart rate and the uterine contraction during the period of pregnancy. It is used to diagnose and classify a fetus state by doctors who have challenges of uncertainty in data. The Rough Neural Network is one of the most common data mining techniques to classify medical data, as it is a good solution for the uncertainty challenge. This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and consumed time during the classification process. WEKA tool is used to analyse cardiotocography data with different algorithms (neural network, decision table, bagging, the nearest neighbour, decision stump and least square support vector machine algorithm). The comparison shows that the accuracy rates and time consumption of the proposed model are feasible and efficient.

Keywords: Accuracy rate; cardiotocography; data mining; rough neural network; WEKA tool

Belal Amin, Mona Gamal, A. A. Salama, I.M. El-Henawy and Khaled Mahfouz, “Classifying Cardiotocography Data based on Rough Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 10(8), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100846

@article{Amin2019,
title = {Classifying Cardiotocography Data based on Rough Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100846},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100846},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {8},
author = {Belal Amin and Mona Gamal and A. A. Salama and I.M. El-Henawy and Khaled Mahfouz}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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