Cardiotocogram improve classification using the neural network classification system
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.
The following chart shows the performance of BL-BPN algorithm. In general, the algorithm gives good
performance for normal and pathological records and poor performance in suspicious records.
Classification Performance of BL-BPN
Precision Recall F-Measure
Normal Suspicious Pathological
Figure 10. Performance of BL-BPN algorithm
The derived results obviously show that the proposed bi-level training improved the classification
performance of system. The BL-BPN approach provided good performance in all cases than compared other
We have evaluated the performance of the four methods with respect to three different metrics. The
performance of standard neural network based classification model, RBF, and SVM were has been compared with
proposed BL-BPN Model. According to the derived results, the performance of the proposed supervised machine
learning based classification approach provided significant performance than other compared methods.
It was found that, the proposed BL-BPN based classifier was capable of identifying Normal, Suspicious
and Pathologic condition, from the nature of CTG data with very good accuracy. If we see the performance of BL-
BPN with respect to all the metrics, then we can say that it almost provided double the performance of the other
three compared methods.
So, future works may address the way to improve the system to recognize the suspicious CTG patterns and treat
them separately while training and testing. One may address the way to improve the system for getting proper
training with different classes of CTG patterns. Future works may address hybrid models using statistical and
machine learning techniques for improved classification accuracy.
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