Abstract
Electrocardiogram (CTG) is a simple and low-cost option to assess the health of the fetus. However, the number of normal fetuses is larger than the number of abnormal fetuses, leading to imbalances in CTG data. Existing studies have attempted to optimize the data processing or model training process by integrating machine learning methods with optimization algorithms. However, the effectiveness of features and appropriate selection of machine learning method creates new challenges. This study proposed an comprehensive method that considers the feature effectiveness and data imbalance issue. The proposed method uses the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the Edited Nearest Neighbours (ENN), Recursive Feature Elimination (RFE), and Artificial Neural Network (ANN) algorithms to find the optimal combination of the parameters of the three algorithms to further improve the accuracy of the fetal health prediction and reduce the cost of tuning. Experimental results show that the algorithm proposed in this paper can effectively solve the imbalance of CTG data, with a classification accuracy of 0.9942 and a kappa measure of 0.9783, which can effectively assist doctors in diagnosing fetal health and improve the quality of hospital visits.
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References
de Campos, D.A., et al.: Sisporto 2.0: a program for automated analysis of cardiotocograms. J. Maternaletal Med. 9(5), 311–18 (2000)
Bach, M., Werner, A., Ywiec, J., Pluskiewicz, W.: The study of under- and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis. Inf. Sci. 384, 174–190 (2016)
Brezočnik, L., Fister, I., Podgorelec, V.: Swarm intelligence algorithms for feature selection: a review. Appl. Sci. 8(9), 1521 (2018)
Chang, C.L., Chen, C.H.: Applying decision tree and neural network to increase quality of dermatologic diagnosis. Exp. Syst. Appl. 36(2 Part 2), 4035–4041 (2009)
Wei, J., et al.: Imbalanced cardiotocography multi-classification for antenatal fetal monitoring using weighted random forest. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds.) ICSH 2019. LNCS, vol. 11924, pp. 75–85. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34482-5_7
Das, S., Mukherjee, H., Obaidullah, S.M., Roy, K., Saha, C.K.: Ensemble based technique for the assessment of fetal health using cardiotocograph – a case study with standard feature reduction techniques. Multimedia Tools Appl. 79(47), 35147–35168 (2020). https://doi.org/10.1007/s11042-020-08853-2
Ersen, Y., Kilikçier, K.: Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Comput. Math. Meth. Med. 2013, 487179 (2013)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)
Huang, M.L., Hsu, Y.Y.: Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network. J. Biomed. Sci. Eng. 05(9), 526–533 (2012)
Kadhim, N., Abed, J.K.: Enhancing the prediction accuracy for cardiotocography (CTG) using firefly algorithm and Naive Bayesian classifier. IOP Conf. Ser. Mater. Sci. Eng. 745(1), 012101 (2020)
Nguyen, B.H., Xue, B., Zhang, M.: A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol. Comput. 54, 100663 (2020)
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)
Ohno, Y., et al.: Assessment of fetal heart rate variability with abdominal fetal electrocardiogram: changes during fetal breathing movement. Asia-Oceania J. Obstet. Gynaecol. 12(2), 301–304 (2010)
Rana, R., Pruthi, J.: Naive Bayes classification (2014)
Richhariya, B., Tanveer, M., Rashid, A.H.: Diagnosis of Alzheimer’s disease using universum support vector machine based recursive feature elimination (USVM-RFE). Biomed. Sig. Process. Control 59, 101903 (2020)
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)
Saunders, C., et al.: Support vector machine. Comput. Sci. 1(4), 1–28 (2002)
Shah, S., Kusiak, A.: Cancer gene search with data-mining and genetic algorithms. Comput. Biol. Med. 37(2), 251–261 (2007)
Signorini, M.G., Magenes, G., Cerutti, S., Arduini, D.: Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings. IEEE Trans. Biomed. Eng. 50(3), 365–374 (2003)
Smith Jr., J.F.: Fetal health assessment using prenatal diagnostic techniques. Curr. Opin. Obstet. Gynecol. 20(2), 152–156 (2008)
Statistics, L.B., Breiman, L.: Random forests. In: Machine Learning, pp. 5–32 (2001)
Steyerberg, E.W., Eijkemans, M., Harrell, F.E., Habbema, J.: Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets. Med. Decis. Making 21(1), 45–56 (2001)
Subasi, A., Kadasa, B., Kremic, E.: Classification of the cardiotocogram data for anticipation of fetal risks using bagging ensemble classifier. Procedia Comput. Sci. 168, 34–39 (2020)
Sylvester, E.V., et al.: Applications of random forest feature selection for fine-scale genetic population assignment. Evol. Appl. 11, 153–165 (2018)
Wheeler, T., Gennser, G., Lindvall, R., Murrills, A.J.: Changes in the fetal heart rate associated with fetal breathing and fetal movement. BJOG: Int. J. Obstet. Gynaecol. 87(12), 1068–1079 (2010)
Zhao, F.Q., Zou, J.H., Yang, Y.H.: A hybrid approach based on artificial neural network (ANN) and differential evolution (DE) for job-shop scheduling problem. Appl. Mech. Mater. 26–28, 754–757 (2010)
Zhou, Q., Hao, Z., Zhou, Q., Fan, Y., Luo, L.: Structure damage detection based on random forest recursive feature elimination. Mech. Syst. Sig. Process. 46(1), 82–90 (2014)
Zou, J., Han, Y., So, S.S.: Overview of artificial neural networks. Meth. Mol. Biol. 458(458), 15 (2009)
Acknowledgements
This study is supported by National Natural Science Foundation of China (71901150, 71702111, 71971143, 71901152), the Natural Science Foundation of Guangdong Province (2020A151501749), Shenzhen University Teaching Reform Project (Grants No. JG2020119) as well as Guangdong Basic and Applied Basic Research Foundation (Project No. 2019A1515011392).
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Gao, J., Huang, C., Huang, X., Huang, K., Wang, H. (2021). Classification of Imbalanced Fetal Health Data by PSO Based Ensemble Recursive Feature Elimination ANN. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_29
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