Open AccessGeometric Mean Maximum FSVMI Model and Its Application in Carotid Artery Stenosis Risk Prediction
Author(s) -
Xueying ZHANG,
Yuling GUO,
Fenglian LI,
Xin WEI,
Fengyun HU,
Haisheng HUI,
Wenhui JIA
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.06.004
Subject(s) - stenosis , transcranial doppler , carotid arteries , artificial intelligence , fuzzy logic , medicine , cardiology , mathematics , radiology , pattern recognition (psychology) , computer science
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.