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Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 14
Year of Publication: 2012
Authors:
Sundar. C
M. Chitradevi
G. Geetharamani
10.5120/7256-0279

Sundar.c, M.chitradevi and G Geetharamani. Article: Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique. International Journal of Computer Applications 47(14):19-25, June 2012. Full text available. BibTeX

@article{key:article,
	author = {Sundar.c and M.chitradevi and G. Geetharamani},
	title = {Article: Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {14},
	pages = {19-25},
	month = {June},
	note = {Full text available}
}

Abstract

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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