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Risk Detection of Stroke using a Feature Selection and Classification Method
Zhang YL(章永来)1; Song WA(宋文爱)1; Li S(李帅)2; Fu, Lizhen1; Li, Shixin3
作者部门数字工厂研究室
关键词Stroke Feature Selection Classification Support Vector Machine Machine Learning
发表期刊IEEE Access
ISSN2169-3536
2018
卷号6页码:31899-31907
收录类别SCI ; EI
EI收录号20181905170361
WOS记录号WOS:000437886800001
产权排序2
资助机构Natural Science Foundation of Shanxi (Grant No. 201601D102031) and the National Natural Science Foundation of China (Grant No. 6160051296).
摘要

Stroke places a heavy burden of care on global societies. Risk detection of stroke is a challenging and time-sensitive task across the world. This article investigated biomedical tests and electronic archives of 792 records that contained 398 records from the five years preceding the onset of stroke at a community hospital. The records included 28 features. We have proposed a new feature selection model that combines support vector machines (SVM) with the glow-worm swarm optimization (GSO) algorithm based on the standard deviation (STD) of the features. The results showed that the proposed model achieved 82.58 accuracy by means of the 18 features among the original dataset. The new map thus represents an effective detection that can help to identify patients with an increased risk of stroke events.

语种英语
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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/21861
专题数字工厂研究室
通讯作者Song WA(宋文爱); Li S(李帅)
作者单位1.North University of China, Taiyuan, 030051, China;
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China;
3.North Automatic Control Technology Institute, Taiyuan, 030006, China.
推荐引用方式
GB/T 7714
Zhang YL,Song WA,Li S,et al. Risk Detection of Stroke using a Feature Selection and Classification Method[J]. IEEE Access,2018,6:31899-31907.
APA Zhang YL,Song WA,Li S,Fu, Lizhen,&Li, Shixin.(2018).Risk Detection of Stroke using a Feature Selection and Classification Method.IEEE Access,6,31899-31907.
MLA Zhang YL,et al."Risk Detection of Stroke using a Feature Selection and Classification Method".IEEE Access 6(2018):31899-31907.
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