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.

  1. G. Georgoulas, D. Stylios, P. Groumpos, Predicting the risk of metabolic acidosis for new borns based on fetal heart rate signal classi?cation using support vector machines, IEEE Trans. Biomed. Eng. 53 (2006) 875884.
  2. S.L. Salzberg, On comparing classi?ers: pitfalls to avoid and a recommended approach, Data Min. Knowl. Discov. (2007) 317328.
  3. M. Cesarelli, M. Romano, P. Bifulco, Comparison of short term variability indexes in cardiotocographic fetal monitoring, Comput. Biol. Med. 39 (2009) 106118.
  4. K. Bache, M. Lichman, Cardiotocography data set, in: UCI Machine Learning Repository, 2010.
  5. N. Krupa, M. Ali, E. Zahedi, S. Ahmed, F.M. Hassan, Antepartum fetal heart rate feature extraction and classi?cation using empirical mode decomposition and support vector machine, Biomed. Eng. Online 10 (2011) 6.
  6. R. Czabanski, J. Jezewski, A. Matonia, M. Jezewski, Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia, Expert Syst. Appl.39 (2012) 1184611860.
  7. C. Sundar . "Performance Evaluation of K-Means and Hierarchal Clustering in Terms of Accuracy and Running Time. "International Journal Computer Science Application (2012)
  8. E. Yilmaz, C. Kilikcier, Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree, Comput. Math. Methods Med. 2013 (2013) 487179.
  9. Tomas Peterek, Petr Gajdos, Pavel Dohnalek, Jana Krohova, Human Fetus Health Classification on Cardiotocographic Data Using Random Forests , Intelligent data analysis and its applications , volume II,pp:189-198,(2014)
  10. Hakan Sahin?, Abdulhamit Subasi , Classi?cation of the cardiotocogram data for anticipation of fetal risks using machine learning techniques , International Burch University, Faculty of Engineering and Information Technologies, Francuske Revolucije b.b., Ilidza, Sarajevo 71000, Bosnia and Herzegovina, Applied Soft Computing 33 (2015) 231238.
  11. V.N. Vapnik and A. Chervonenkis, "A note on one class of perceptrons", Automation and Remote Control, 25, 1964
  12. J.R.Quinlan, "Induction of decision tree". Journal of Machine Learning 1, 1986,
  13. V. N. Vapnik, "The Nature of Statistical Learning Theory", Springer, New York, NY, USA, 1995.
  14. Mark A. Hall, Lloyd A. Smith, Feature Subset Selection: A Correlation Based Filter Approach, In 1997 International Conference on Neural Information Processing and Intelligent Information Systems (1997), pp. 855-858.
  15. Han and Kamber, - "Data Mining; Concepts and Techniques", Morgan Kaufmann Publishers, 2000.
  16. H. Blockeel and J. Struyf, "Efficient algorithms for decision tree cross-validation", Proceedings of the Eighteenth International Conference on Machine Learning (C. Brodley and A. Danyluk, eds.), Morgan Kaufmann, 2001, pp. 11-18.

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.
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