Abstract
Aiming at the imbalance and cost-sensitive problem of sample categories in actual fetal monitoring, as well as actual needs, we proposed a category imbalance fetal contraction monitoring model based on GBDT (Gradient Boosting Decision Tree) combined learning. Subsets with balanced category were generated by random under-sampling and applied to train several GBDT base classifiers using the method of feature selection. We integrated the base classifiers by the simple average method and calculated the final prediction probability. In this study, AUC and cost-sensitive error rate were used as evaluation indicators to compare with the commonly used single learning models such as Decision Tree, Logistic Regression and combined learning models like Random Forest to verify the effectiveness of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
China Statistical Yearbook: China Statistical Publishing House, Beijing (2017)
Alfirevic, Z., Devane, D., Gyte, G.M.L.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database Syst. Rev. 5(3), CD006066 (2006)
Li-Jun, W., Ming-Quan, C., An-Bo, L.: Study of continuous electronic heart rate monitoring during delivery period in rural district. Chin. J. Obstet. Gynecol. Pediatrics (2007)
Umstad, M.P.: The predictive value of abnormal fetal heart rate patterns in early labour. Aust. N. Z. J. Obstet. Gynaecol. 33(2), 145–149 (2010)
Fergus, P., Hussain, A., Al-Jumeily, D., Huang, D.-S., Bouguila, N.: Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms. Biomed. Eng. Online 16(1), 89 (2017)
Ocak, H., Ertunc, H.M.: Prediction of fetal state from the cardiotocogram recordings using adaptive neuro -fuzzy inference s ystems. Neural Comput. Appl. 23(6), 1583–1589 (2013)
Ocak, H.: A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. J. Med. Syst. 37(2), 9913 (2013)
Yılmaz, E.: Fetal state assessment from cardiotocogram data using artificial neural networks. J. Med. Biol. Eng. 36(6), 820–832 (2016)
Huang, M.L., Hsu, Y.Y.: Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network. J. Biomed. Sci. Eng. 05(9) (2012)
Sundar, C., Chitradevi, M., Geetharamani G.: Classification of cardiotocogram data using neural network based machine learning technique. Int. J. Comput. Appl. 47(14), 19–25 (2013)
Karabulut, E.M., Ibrikci, T.: Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach. J. Comput. Commun. 02(9), 32–37 (2014)
Arif, M.: Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal. Biomater. Biomech. Bioeng. 2(3), 173–183 (2015)
Yılmaz, E.: Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Comput. Math. Methods Med. 2013(2), 487179 (2013)
Ravindran, S., Jambek, A.B., Muthusamy, H., et al.: A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being. Comput. Math. Methods Med. 2015 (2015)
Lessmann, S., Baesens, B., Scow, H.V., et al.: Benehmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 1(32) (2015)
Krebs, H.B., Petres, R.E.: Clinical application of a scoring system for evaluation of antepartum fetal heart rate monitoring. Am. J. Obstet. Gynecol. 130(7), 765–72 (1978)
Sahin, H., Subasi, A.: Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Appl. Soft Comput. 33(C), 231–238 (2015)
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor. News·Lett. 6(1), 1–6 (2004)
Acknowledgment
This work is partially supported by a grant from the Natural Science Foundation of Guangdong Province (grant no. 2018A0303130055), the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing at Sun Yat-sen University (No. 202001) and the Social Science Project of Guangzhou University of Chinese Medicine grants 2020SKYB05 and 2020SKXK25.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Qin, C., Liu, S., Lin, S., Li, G., Hong, J. (2021). A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_24
Download citation
DOI: https://doi.org/10.1007/978-981-16-7502-7_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7501-0
Online ISBN: 978-981-16-7502-7
eBook Packages: Computer ScienceComputer Science (R0)