Gmean Maximum FSVMI Model and Its Application for Carotid Artery Stenosis Risk Prediction

ZHANG Xueying; GUO Yuling; LI Fenglian; WEI Xin; HU Fengyun; HUI Haisheng; JIA Wenhui

doi:10.23919/cje.2020.00.185

Article Contents

Article Navigation > Chinese Journal of Electronics > 2022 > Corrected proof

ZHANG Xueying, GUO Yuling, LI Fenglian, et al., “Gmean Maximum FSVMI Model and Its Application for Carotid Artery Stenosis Risk Prediction,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2020.00.185, 2022.

Citation:

ZHANG Xueying, GUO Yuling, LI Fenglian, et al., “Gmean Maximum FSVMI Model and Its Application for Carotid Artery Stenosis Risk Prediction,” Chinese Journal of Electronics, in press, doi: 10.23919/cje.2020.00.185, 2022.

Citation:

PDF( 3967 KB)

Gmean Maximum FSVMI Model and Its Application for Carotid Artery Stenosis Risk Prediction

doi: 10.23919/cje.2020.00.185

1.
College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China
2.
Department of Neurology, Shanxi Province People’s Hospital, Taiyuan 030012, China

Funds: This work was supported in part by the Key Research and Development Project of Shanxi Province, China (201803D31045, 201603D321060), the Natural Science Foundation of Shanxi Province, China (201801D121138), and the Project of Youth Fund of Shanxi Health Commission (201301029)

More Information

Author Bio:
received the Ph.D. degree in underwater acoustic engineering from Harbin Engineering University, Harbin, China, in 1998. She is currently a Professor with the College of Information and Computer, Taiyuan University of Technology. Her research interests include auditory modeling, emotional speech recognition, and so on. (Email: tyzhangxy@163.com)

received the B.E. degree from Tianjin Normal University, in 2018. She is a M.E. candidate of the Taiyuan University of Technology. Her research interests include machine learning, EEG analysis, and their applications. (Email: 1102921772@qq.com)

(corresponding author) received the Ph.D. degree in electronic science and technology from the Taiyuan University of Technology (TYUT), Taiyuan, China, in 2010. She is currently a Professor with the College of Information and Computer, TYUT. Her research interests include classification model over imbalanced data sets, and medical signal processing. (Email: ghllfl@163.com)

received the M.E. degree from the Taiyuan University of Technology, China, in 2019. His research interests include big data analysis and data mining. (Email: 1224134552@qq.com)

received the MBBS degree in clinical medicine from the Shanxi Medical College, Taiyuan, China, in 1983. She is currently a Chief Physician with the Shanxi Provincial People’s Hospital. Her research interests include diagnosis and treatment of cerebrovascular disease, epilepsy, and neurosis. (Email: fengyun71@163.com)

received the M.E. degree from the Taiyuan University of Technology (TYUT), China, in 2012. He is a Ph.D. candidate of the TYUT. He had five years of experience in software development. His research interests include medical image analysis, artificial intelligence, and their applications in medical practice. (Email: huihaisheng@163.com)

received the M.E. degree from the Dalian Medical University, China, in 2008. He is currently an Associate Chief Physician with the Shanxi Provincial People’s Hospital. His research interests include etiology and pathogenesis of youth stroke. (Email: jiawenhui0806@163.com)
Received Date: 2020-06-22
Accepted Date: 2020-11-03

Available Online: 2022-01-11

Abstract

Abstract

Carotid artery stenosis is a serious medical condition that can lead to stroke. Using machine learning method to construct classifier model, carotid artery stenosis can be diagnosed with transcranial doppler data. We propose an improved fuzzy support vector machine (FSVMI) model to predict carotid artery stenosis, with the maximum geometric mean (Gmean) as the optimization target. The fuzzy membership function is obtained by combining information entropy with the normalized class-center distance. Experimental results showed that the proposed model was superior to the benchmark models in sensitivity and geometric mean criteria.

FullText(HTML)

References(28)

References

[1]	H. C. Zhou, L. Shen, F. Wei, et al., “Predicting the risk of stroke in Chinese internal carotid artery stenosis patients underwent carotid artery stenting: Validation and improvement of siena carotid artery stenting risk score,” Journal of Stroke and Cerebrovascular Diseases, vol.28, no.12, article no.104369, 2019. (in Chinese) doi: 10.1016/j.jstrokecerebrovasdis.2019.104369
[2]	M. Moradi, M. M. B. Mahdavi, and M. K. M. Nogourani, “The relation of calcium volume score and stenosis of carotid artery,” Journal of Stroke and Cerebrovascular Diseases, vol.29, no.1, article no.104493, 2020. doi: 10.1016/j.jstrokecerebrovasdis.2019.104493
[3]	C de la Cruz Cosme, M S Dawid Milner, G Ojeda Burgos, et al., “Validation of a basic neurosonology laboratory for detecting cervical carotid artery stenosis,” Neurologia, vol.34, no.6, pp.367–375, 2019. doi: 10.1016/j.nrl.2017.01.015
[4]	Y. Hanefi, H.B. Altynsoy, N. Barp, et al., “Classification of the frequency of carotid artery stenosis with MLP and RBF neural networks in patients with coroner artery disease,” Journal of Medical Systems, vol.28, no.6, pp.591–601, 2004. doi: 10.1023/B:JOMS.0000044961.38008.97
[5]	U. Ergün, S. Serhatloglu, F. Hardalac, et al., “Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression,” Computers in Biology and Medicine, vol.34, no.5, pp.389–405, 2004. doi: 10.1016/S0010-4825(03)00085-4
[6]	U. Harun and K. Halife, “Classification of Internal carotid artery Doppler signals using hidden Markov model and wavelet transform with entropy”, 4th International Conference on Advances in Information Technology (IAIT 2010), Bangkok, Thailand, pp.183–191, 2010.
[7]	Y. K. Sun, Y. Cao, G. Xie, et al., “Condition monitoring for railway point machines based on sound analysis and support vector machine,” Chinese Journal of Electronics, vol.29, no.4, pp.786–792, 2020. doi: 10.1049/cje.2020.06.007
[8]	S. B. Huang, Y. Li, and Y. M. Li, “An SVM-based prediction method for solving SAT problems,” Chinese Journal of Electronics, vol.28, no.2, pp.246–252, 2019. doi: 10.1049/cje.2019.01.004
[9]	H. G. Xiao, A. Avolio, and D. C. Huang, “A novel method of artery stenosis diagnosis using transfer function and support vector machine based on transmission line model: A numerical simulation and validation study,” Computer Methods and Programs in Biomedicine, vol.129, pp.71–81, 2016. doi: 10.1016/j.cmpb.2016.03.005
[10]	K. C. Hsu, C. H. Lin, K. R. Johnson, et al., “Autodetect extracranial and intracranial artery stenosis by machine learning using ultrasound,” Computers in Biology and Medicine, vol.116, article no.103569, 2019. doi: 10.1016/j.compbiomed.2019.103569
[11]	C. F. Lin and S. D. Wang, “Fuzzy support vector machines,” IEEE Transactions on Neural Networks, vol.13, no.2, pp.464–471, 2002. doi: 10.1109/72.991432
[12]	X. Y. Zhang, X. Wei, F. L. Li, et al., “Fuzzy support vector machine with imbalanced regulator and its application in stroke classification,” 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), Newark, CA, USA, pp.290–295, 2019.
[13]	X. F. Jiang, Y. Zhang, and C. L. Jian, “Fuzzy SVM with a new fuzzy membership function,” Neural Computing and Applications, vol.15, no.3-4, pp.268–276, 2006. doi: 10.1007/s00521-006-0028-z
[14]	X. G. Zhang, “Using class-center vectors to build support vector machines,” Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop, Madison, WI, USA, pp.3–11, 1999.
[15]	C. M. Zhu and Z. Wang, “Entropy-based matrix learning machine for imbalanced data sets,” Pattern Recognition Letters, vol.88, pp.72–80, 2017. doi: 10.1016/j.patrec.2017.01.014
[16]	B. Simon, C. Clément, A. Sébastien, et al., “The multiclass roc front method for cost-sensitive classification,” Pattern Recognition, vol.52, pp.46–60, 2016. doi: 10.1016/j.patcog.2015.10.010
[17]	J. J. Hu, H. Q. Yang, M. R. Lyu, et al., “Online nonlinear AUC maximization for imbalanced data sets,” IEEE Transactions on Neural Networks and Learning Systems, vol.29, no.4, pp.882–895, 2017. doi: 10.1109/TNNLS.2016.2610465
[18]	G. Niccolò and V. Valerio, “Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry,” Industrial Marketing Management, vol.62, pp.100–107, 2017. doi: 10.1016/j.indmarman.2016.08.003
[19]	N. Harikrishna and A. Shivani, “Support vector algorithms for optimizing the partial area under the ROC curve,” Neural computation, vol.29, no.7, pp.1919–1963, 2017. doi: 10.1162/NECO_a_00972
[20]	Y. J. Tian, Y. Shi, X. J. Chen, et al., “AUC maximizing support vector machines with feature selection,” Procedia Computer Science, vol.4, pp.1691–1698, 2011. doi: 10.1016/j.procs.2011.04.183
[21]	Q. Fan, Z. Wang, D. D. Li, et al., “Entropy-based fuzzy support vector machine for imbalanced datasets,” Knowledge Based Systems, vol.115, pp.87–99, 2017. doi: 10.1016/j.knosys.2016.09.032
[22]	X. Zhang, X. L. Xiao, and G. Y. Xu, “Fuzzy support vector machine based on affinity among samples,” Journal of Software, vol.17, no.5, pp.951–958, 2006. doi: 10.1360/jos170951
[23]	H. Y. Yu, C. Y. Sun, X. B. Yang, et al., “Fuzzy support vector machine with relative density information for classifying imbalanced data,” IEEE Transactions on Fuzzy Systems, vol.27, no.12, pp.2353–2367, 2019. doi: 10.1109/TFUZZ.2019.2898371
[24]	B. Rukshan and P. Vasile, “FSVM-CIL: Fuzzy support vector machines for class imbalance learning,” IEEE Transactions on Fuzzy Systems, vol.18, no.3, pp.558–571, 2010. doi: 10.1109/TFUZZ.2010.2042721
[25]	B. Rukshan and P. Vasile, “Class imbalance learning methods for support vector machines,” John and Wiley, Sons and Inc, pp.83–99, 2013.
[26]	Y. L. Li, J. Feng, Y. Ren, et al., “Breast cancer detection based on mixture membership function with MFSVM-FKNN ensemble classifier,” 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China, pp.297–301, 2012.
[27]	X. Liu, Z. S. Pan, H. M. Yang, et al., “An adaptive moment estimation method for online AUC maximization,” PloS One, vol.14, no.4, article no.e0215426, 2019. doi: 10.1371/journal.pone.0215426
[28]	A. F. Jesús, F. Alberto, L. Julián, et al., “Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework,” Journal of Multiple-Valued Logic & Soft Computing, vol.17, no.2, pp.255–287, 2011.