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Reseach Article

Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique

by Sundar.c, M.chitradevi, G. Geetharamani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 14
Year of Publication: 2012
Authors: Sundar.c, M.chitradevi, G. Geetharamani

Sundar.c, M.chitradevi, G. Geetharamani . Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique. International Journal of Computer Applications. 47, 14 ( June 2012), 19-25. DOI=10.5120/7256-0279

@article{ 10.5120/7256-0279,
author = { Sundar.c, M.chitradevi, G. Geetharamani },
title = { Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 14 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-25 },
numpages = {9},
url = { },
doi = { 10.5120/7256-0279 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:41:51.180929+05:30
%A Sundar.c
%A M.chitradevi
%A G. Geetharamani
%T Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 14
%P 19-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

Cardiotocography (CTG) is a simultaneous recording of fetal heart rate (FHR) and uterine contractions (UC). It is one of the most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. Even few decades after the introduction of cardiotocography into clinical practice, the predictive capacity of the these methods remains controversial and still inaccurate. In this paper, we implement a model based CTG data classification system using a supervised artificial neural network(ANN) which can classify the CTG data based on its training data. According to the arrived results, the performance of the supervised machine learning based classification approach provided significant performance. We used Precision, Recall, F-Score and Rand Index as the metric to evaluate the performance. It was found that, the ANN based classifier was capable of identifying Normal, Suspicious and Pathologic condition, from the nature of CRG data with very good accuracy.

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Index Terms

Computer Science
Information Sciences


Multidimensional Data Classification Medical Data Classification Cardiotocography Ctg Fetal Heart Rate Fhr. Uterine Contractions Uc Ann