دورية
Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction
| العنوان: | Geometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction |
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| المؤلفون: | Xueying, ZHANG, Yuling, GUO, Fenglian, LI, Xin, WEI, Fengyun, HU, Haisheng, HUI, Wenhui, JIA |
| المصدر: | Chinese Journal of Electronics; September 2021, Vol. 30 Issue: 5 p824-832, 9p |
| مستخلص: | 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. |
| قاعدة البيانات: | Supplemental Index |
| تدمد: | 10224653 20755597 |
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| DOI: | 10.1049/cje.2021.06.004 |