An Outlier Based
An Outlier Based Bi-Level Neural Network Classification
System for Improved Classification of Cardiotocogram Data
Sundar Chinnasamy1’*,
Chitradevi Muthusamy2 and Geetharamani Gopal3
1Christian College of Engineering and Technology,
Oddanchatram – 624619, Tamil Nadu, India.
2PRIST University, Trichy Campus – Tamilnadu, Trichiy –
620009, Tamil Nadu, India.
3Anna University Chennai, BIT Camps, Trichy – 620024,
Tamil Nadu, India.
*Corresponding Author. E-Mail: [email protected]
Abstract: Cardiotocography
(CTG), consisting of fetal heart rate (FHR) and tocographic (TOCO)
measurements, is used to evaluate fetal well-being. 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. Even few decades after the
introduction of cardiotocography into clinical practice, the predictive
capacity of the existing methods remains inaccurate. In a previous work
(Sundar.C and et al, 2012), we showed that a model based CTG data
classification system using a supervised artificial neural network (ANN) can
classify the CTG data better than most of the other methods. But, the
performance of the normal neural network based classifier was limited because
of the presence of potential outliers in the training data. The presence of
outliers in training data affects the neural network training as well as
testing. In this work, we present improved classification models which will
consider outliers in the data and eliminate them from training phase of the
classification process. We used Precision, Recall, F-Score and Rand Index as
the metric to evaluate the performance. The proposed idea considerably improved
the performance in classifying Normal, Suspicious and Pathologic CTG
patterns. It was found that, the improved classifier was capable of
identifying Normal, Suspicious and Pathologic condition with very good accuracy
than normal methods.
[Sundar C, Chitradevi M, Geetharamani G. An
Outlier Based Bi-Level Neural Network Classification System for Improved
Classification of Cardiotocogram Data. Life Sci J 2013;10(1):244-251]
(ISSN:1097-8135).http://www.lifesciencesite.com. 37
Keywords: Outlier Detection; CTG; BPN; Dimensionality Reduction;
RBF Full Text 37
