Comparative Study between Various Classification Algorithms for Classification of Cardiotocogram Data

Authors(1) :-Jagannathan D

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 SVM, Decision Tree, MLP and Navie Bayes which can classify the CTG data based on its training data. We used specificity, NPV, Precision, Recall, G-Mean, F-Measure and ROC 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 CTG data with very good accuracy.

Authors and Affiliations

Jagannathan D
M.Phil. (PG Scholar), Department of Computer Science, Dr. C. V. Raman University, Chhattisgarh, India

CTG, Data mining, Classification, Support Vector Machine, Decision Tree, Multilayer Perceptron and Navie Bayes.

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 651-657
Manuscript Number : CSEIT1724172
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Jagannathan D, "Comparative Study between Various Classification Algorithms for Classification of Cardiotocogram Data ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.651-657, July-August-2017.
Journal URL : http://ijsrcseit.com/CSEIT1724172

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