Risk Detection of Stroke using a Feature Selection and Classification Method
文献类型：期刊论文
| 作者 | Song WA(宋文爱); Zhang YL(章永来); Li S(李帅); Fu, Lizhen; Li, Shixin; Zheng R(郑荣) ; Lin Y(林扬); Gao QS(高启升); Zhu XH(朱兴华); Gu HT(谷海涛)
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| 刊名 | IEEE Access
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| 出版日期 | 2018 |
| 卷号 | 6页码:31899-31907 |
| 关键词 | Stroke feature selection classification support vector machine machine learning |
| ISSN号 | 2169-3536 |
| 产权排序 | 2 |
| 通讯作者 | Song WA(宋文爱) ; Li S(李帅) |
| 中文摘要 | 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. |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000437886800001 |
| 源URL | [http://ir.sia.cn/handle/173321/21838]  |
| 专题 | 沈阳自动化研究所_海洋信息技术装备中心 |
| 通讯作者 | Gao QS(高启升) |
| 作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China 2.North University of China, Taiyuan, 030051, China 3.North Automatic Control Technology Institute, Taiyuan, 030006, China. |
| 推荐引用方式 GB/T 7714 | Song WA,Zhang YL,Li S,et al. Risk Detection of Stroke using a Feature Selection and Classification Method[J]. IEEE Access,2018,6:31899-31907. |
| APA | Song WA.,Zhang YL.,Li S.,Fu, Lizhen.,Li, Shixin.,...&谷海涛.(2018).Risk Detection of Stroke using a Feature Selection and Classification Method.IEEE Access,6,31899-31907. |
| MLA | Song WA,et al."Risk Detection of Stroke using a Feature Selection and Classification Method".IEEE Access 6(2018):31899-31907. |
入库方式： OAI收割
来源：沈阳自动化研究所
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