Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction

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

doi:10.1049/cje.2021.06.004

Volume 30 Issue 5

Sep. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(5): 824-832

ZHANG Xueying, GUO Yuling, LI Fenglian, et al., “Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 824-832, 2021, doi: 10.1049/cje.2021.06.004

Citation:

ZHANG Xueying, GUO Yuling, LI Fenglian, et al., “Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 824-832, 2021, doi: 10.1049/cje.2021.06.004

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Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction

doi: 10.1049/cje.2021.06.004

1. College of Information and Computer, Taiyuan University of Technology, Jinzhong 030024, China;
2. Department of Neurology, Shanxi Province People's Hospital, Taiyuan 030012, China

Funds:

This work is supported by the Key Research and Development Project of Shanxi Province, China (No.201803D31045, No.201603D321060), the Natural Science Foundation of Shanxi Province, China (No.201801D121138), and the Project of Youth Fund of Shanxi Health Commission (No.201301029).

Received Date: 2020-06-22
Available Online: 2021-09-02

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 model to predict carotid artery stenosis, with the maximum geometric mean 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.

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References

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