Share This Article:

Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network

Abstract Full-Text HTML Download Download as PDF (Size:772KB) PP. 526-533
DOI: 10.4236/jbise.2012.59065    4,757 Downloads   7,320 Views   Citations

ABSTRACT

Fetal distress is one of the main factors to cesarean section in obstetrics and gynecology. If the fetus lack of oxygen in uterus, threat to the fetal health and fetal death could happen. Cardiotocography (CTG) is the most widely used technique to monitor the fetal health and fetal heart rate (FHR) is an important index to identify occurs of fetal distress. This study is to propose discriminant analysis (DA), decision tree (DT), and artificial neural network (ANN) to evaluate fetal distress. The results show that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78%, respectively.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Huang, M. and Hsu, Y. (2012) Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network. Journal of Biomedical Science and Engineering, 5, 526-533. doi: 10.4236/jbise.2012.59065.

References

[1] Maimon, O. and Rokach, L. (2005) The Data mining and knowledge discovery handbook. Springer, New York. doi:10.1007/b107408
[2] Chandraharan, E. and Arulkumaran, S. (2007) Prevention of birth asphyxia: Responding appropriately to cardiotodograph (CTG) traces. Best Practice & Research Clinical Obstetrics and Gynaecology, 21, 609-624. doi:10.1016/j.bpobgyn.2007.02.008
[3] Lunghi, F., Magenes, G., Pedrinazzi, L. and Signorini, M. G. (2005) Detection of fetal distress though a support vector machine based on fetal heart rate parameters. Computers in Cardiology, 25-28 September 2005, 247-250. doi:10.1109/CIC.2005.1588083
[4] Vigil-De Gracia, P., Simití, E. and Lora, Y. (2000) Intrapartum fetal distress and magnesium sulfate. International Journal of Gynecology & Obstetrics, 68, 3-6.
[5] Romano, M., Bracale, M., Cesarelli, M., et al. (2006) Antepartum cardiotocography: A study of fetal reactivity in frequency domain. Computers in Biology and Medicine, 36, 619-633. doi:10.1016/j.compbiomed.2005.04.004
[6] Sueyoshi, T. (2001) Extend DEA-discriminant analysis. European Journal of Operational Research, 131, 324-351. doi:10.1016/S0377-2217(00)00054-0
[7] Polat, K., Günes S. and Arslan, A. (2008) A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vec tor machine. Expert System with Applications, 34, 482-487. doi:10.1016/j.eswa.2006.09.012
[8] Piacenti da Sliva, M., Zucchi, O.L.A.D., Ribeiro-Silva, A., et al. (2009) Discriminant analysis of trace elements in normal, benign and malignant breast tissues measured by total reflection X-ray fluorescence. Spectrochimica Acta Part B, 64, 587-592. doi:10.1016/j.sab.2009.05.026
[9] Chang, C.L. and Chen, C.H. (2009) Applying decision tree and neural network to increase quality of dermatologic diagnosis. Expert Systems with Applications, 36, 4035-4041. doi:10.1016/j.eswa.2008.03.007
[10] Atkins, J.P., Burdon, D. and Allen, J.H. (2007) An application of contingent valuation and decision tree analysis to water quality improvement. Marine Pollution Bulletin, 55, pp. 591-602. doi:10.1016/j.marpolbul.2007.09.018
[11] Waheed, T., Bonnell, R.B., Prasher, S.O., Paulet, E. (2006) Measuring performance in precision agriculture: CART-A decision tree approach. Agricultural water management, 84, 173-185. doi:10.1016/j.agwat.2005.12.003
[12] Kohonen, T. (1988) An introduction to neural computing. Neural Networks, 1, pp. 3-6. doi:10.1016/0893-6080(88)90020-2
[13] Lin, C.C., Ou, Y.K., Chen, S.H., et al. (2010) Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture. International Journal of the Care of the Injured, 41, 869-873.
[14] Frank, A. and Asuncion, A. (2010) UCI Machine Learning Repository. University of California, School of Information and Computer Science. Irvine. http://archive.ics.uci.edu/ ml
[15] Kleinbaum, D.G., Kupper, L.L. and Muller, K.E. (1998) Applied regression analysis and other multivariate methods. 2nd Edition, PSW-Kent, Boston.
[16] Yeh, Y.C. (2003) Application and practice of artificial neural network. Scholar Books Co., Ltd., Taipei.
[17] Lu, T., Chen, X. and Zhou, S. (2010) Optimization for impact factors of dam deformation based on BP neural network model. International Conference on Intelligent Computation Technology and Automation, Changsha, 11-12 May 2010, 854-857.
[18] Paola, J.D. (1994) Neural network classification of multispectral imagery. Master’s Thesis, University of Arizona, Tucson.
[19] Wang, C. (1994) A theory of generalization in learning machine with neural application. Ph.D Thesis, University of Pennsylvania, Philadelphia.
[20] Yarnguy, T. and Kanarkard, W. (2011) A radial basis function committee machine for cardiotocography classification. The 12th Graduate Research Conference, Khon Kaen University, Khon Kaen, 262-267.
[21] Huang, M.L., Hung, Y.H. and Chen, W.Y. (2010) Neural network classifier with entropy based feature selection on breast cancer diagnosis. Journal of Medical Systems, 34, 865-873. doi:10.1007/s10916-009-9301-x
[22] Huang, M.L. and Chen, H.Y. (2011) Glaucoma Classification model based on GDx VCC measured parameter by decision tree. Journal of Medical Systems, 34, 1141-1147. doi:10.1007/s10916-009-9333-2

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.