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@example.com
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