Multi-Classification of Fetal Health Status Using Extreme Learning Machine

Authors

  • Ömer KASIM Dumlupınar Üniversitesi

DOI:

https://doi.org/10.46291/ICONTECHvol5iss2pp62-70

Keywords:

Cardiotocography, Fetal Health Status, Extreme Machine Learning, Computer aided diagnosis.

Abstract

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive.  When multi-class classification was performed, the accuracy was obtained as 98.12%.  The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

References

Arif, M. 2015. Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal. Biomaterials and Biomechanics in Bioengineering, 2(3):173-183.

Arif, M. Z., Ahmed, R., Sadia, U. H., Tultul, M. S. I., and Chakma, R. 2020. Decision Tree Method Using for Fetal State Classification from Cardiotography Data. Journal of Advanced Engineering and Computation, 4(1): 64-73.

Ashwal, E., Shinar, S., Aviram, A., Orbach, S., Yogev, Y., and Hiersch, L. 2019. A novel modality for intrapartum fetal heart rate monitoring. The Journal of Maternal-Fetal & Neonatal Medicine, 32(6): 889-895.

Avuçlu, E., and Abdullah, E. 2020. Classification of Cardiotocography Records with Naïve Bayes. International Scientific and Vocational Studies Journal, 3(2): 105-110.

Ayres-de-Campos, D., Bernardes, J., Garrido, A., Marques-de-Sa, J., and Pereira-Leite, L. 2000. SisPorto 2.0: a program for automated analysis of cardiotocograms. Journal of Maternal-Fetal Medicine, 9(5): 311-318.

Ayres-de-Campos, D., Costa-Santos, C., and Bernardes, J., Multicentre 2005. Prediction of neonatal state by computer analysis of fetal heart rate tracings: the antepartum arm of the SisPorto® multicentre validation study. European Journal of Obstetrics & Gynecology and Reproductive Biology, 118(1): 52-60.

Bhatnagar, D., and Maheshwari, P. 2016. Classification of cardiotocography data with WEKA. International Journal of Computer Science and Network-IJCSN, 5(2):412-418.

Chamidah, N., and Wasito, I. 2015. Fetal state classification from cardiotocography based on feature extraction using hybrid K-Means and support vector machine. In 2015 international conference on advanced computer science and information systems (ICACSIS) : 37-41.

Cömert, Z., and Kocamaz, A. F. 2017. Comparison of machine learning techniques for fetal heart rate classification. Acta Phys. Pol. A, 132(3): 451-454.

Çil, B., Ayyıldız, H., and Tuncer, T. (2020). Discrimination of β-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system. Medical hypotheses, 138: 109611.

Fergus, P., Hussain, A., Al-Jumeily, D., Huang, D. S., and Bouguila, N. 2017. Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms. Biomedical engineering online, 16(1): 1-26.

Georgoulas, G., Stylios, C., Chudacek, V., Macas, M., Bernardes, J., & Lhotska, L. 2007. Classification of fetal heart rate signals based on features selected using the binary particle swarm algorithm. In World Congress on Medical Physics and Biomedical Engineering 2006 :1156-1159. Springer, Berlin, Heidelberg.

Huang, G. B., Zhu, Q. Y., Siew, C. K. 2006. Extreme learning machine: theory and applications. Neurocomputing, 70(1-3): 489-501.

Jagannathan D. 2018. Cardiotocography - a comparative study between support vector machine and decision tree algorithms. International Journal of Trend in Research and Development, 4: 148-151.

Kannan, E., Ravikumar, S., Anitha, A., Kumar, S. A., and Vijayasarathy, M. 2021. Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set. Journal of Ambient Intelligence and Humanized Computing: 1-13.

Kapaya, H., Jacques, R., Almond, T., Rosser, M. H., and Anumba, D. (2020). Is short-term-variation of fetal-heart-rate a better predictor of fetal acidaemia in labour? A feasibility study. Plos one, 15(8): e0236982.

Karabulut, E. M., and Ibrikci, T. 2014. Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach. Journal of Computer and Communications, 2(9): 32-37.

Menai, M. E. B., Mohder, F. J., and Al-mutairi, F. 2013. Influence of feature selection on naïve Bayes classifier for recognizing patterns in cardiotocograms. Journal of Medical and Bioengineering, 2(1):66-70.

Nandipati, S. C. R., & XinYing, C. 2020. Classification and Feature Selection Approaches for Cardiotocography by Machine Learning Techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 12(1): 7-14.

Sahin H., and Subasi A. 2015. Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques, Applied Soft Computing, 33: 231-238.

Shah, S. A. A., Aziz, W., Arif, M., and Nadeem, M. S. A. 2015. Decision trees based classification of cardiotocograms using bagging approach. In 2015 13th international conference on frontiers of information technology (FIT):12-17.

Subha, V., Murugan, D., Rani, J., Rajalakshmi, K., and Tirunelveli, T. 2013. Comparative analysis of classification techniques using Cardiotocography dataset. International Journal of Research in Information Technology, 1(12): 274-280.

UCI Machine Learning Repository: Cardiotocography Data set, Available: http://archive.ics.uci.edu/ml/datasets/Cardiotocography, (2021, March).

Wu, X., Liu, Y., Kearfott, K., and Sun, X. 2020. Evaluation of public dose from FHR tritium release with consideration of meteorological uncertainties. Science of the Total Environment, 709: 136085.

Zhang, Y., and Zhao, Z. 2017. Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost. In 2017 10th international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI): 1-6.

Zhong, W., Liao, L., Guo, X., and Wang, G. 2019. Fetal electrocardiography extraction with residual convolutional encoder–decoder networks. Australasian physical & engineering sciences in medicine, 42(4): 1081-1089.

Published

2021-06-28

How to Cite

KASIM, Ömer. (2021). Multi-Classification of Fetal Health Status Using Extreme Learning Machine. ICONTECH INTERNATIONAL JOURNAL, 5(2), 62-70. https://doi.org/10.46291/ICONTECHvol5iss2pp62-70

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Section

Articles