Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach

Esra Mahsereci Karabulut, Turgay Ibrikci

Computer Programming Department, Gaziantep University, Gaziantep, Turkey.
Department of Electrical and Electronics Engineering, Cukurova University, Adana, Turkey.

DOI: 10.4236/jcc.2014.29005                

Abstract

Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate (FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to iden- tifying fetuses which suffer from lack of oxygen, i.e. hypoxia. This situation is defined as fetal dis- tress and requires fetal intervention in order to prevent fetus death or other neurological disease caused by hypoxia. In this study a computer-based approach for analyzing cardiotocogram in- cluding diagnostic features for discriminating a pathologic fetus. In order to achieve this aim adaptive boosting ensemble of decision trees and various other machine learning algorithms are employed.

Keywords

Cardiotocogram, Fetal Distress, Adaptive Boosting, Decision Tree

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Steer, P.J. (2008) Has Electronic Fetal Heart Rate Monitoring Made a Difference? Seminars in Fetal and Neonatal Medicine, 13, WB Saunders, 2-7.
[2]	Huang, M. and Hsu, Y. (2012) Fetal Distress Prediction Using Discriminant Analysis, Decision Tree, and Artificial Neural Network. Journal of Biomedical Science & Engineering, 5, 526-533. http://dx.doi.org/10.4236/jbise.2012.59065
[3]	Sundar, C., Chitradevi, M. and Geetharamani, G. (2012) Classification of Cardiotocogram Data Using Neural Network Based Machine Learning Technique. International Journal of Computer Applications, 47, 19-25. http://dx.doi.org/10.5120/7256-0279
[4]	Sundar, C., Chitradevi, M. and Geetharamani, G. (2013) An Overview of Research Challenges for Classification of Cardiotocogram Data. Journal of Computer Science, 9, 198-206. http://dx.doi.org/10.3844/jcssp.2013.198.206
[5]	Y?lmaz, E. and K?l?k??er, ?. (2013) Determination of Fetal State from Cardiotocogram Using LS-SVM with Particle Swarm Optimization and Binary Decision Tree. Computational and Mathematical Methods in Medicine, 2013, 2013. http://dx.doi.org/10.1155/2013/487179
[6]	Newman, D.J., Heittech, S., Blake, C.L. and Merz, C.J. (1998) UCI Repository of Machine Learning Databases. University California Irvine, Department of Information and Computer Science.
[7]	Freund, Y. and Schapire, R. (1996) Experiments with a New Boosting Algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, 1996, 148-156.
[8]	Kuncheva, L. (2004) Combining Pattern Classifiers Methods and Algorithms. Wiley-Interscience, 360. http://dx.doi.org/10.1002/0471660264
[9]	Duda, O.R., Hart, P.E. and Stork, D.G. (2006) Pattern Classification. John Wiley & Sons Inc.
[10]	Tanner, L., et al. (2008) Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness. PLoS Neglected Tropical Diseases, 2, e196. http://dx.doi.org/10.1371/journal.pntd.0000196
[11]	Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11, 1.
[12]	Wang, C.W. (2006) New Ensemble Machine Learning Method for Classification and Prediction on Gene Expression Data. Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE, 2006, 3478-3481.