Open AccessRisk Detection of Stroke Using a Feature Selection and Classification Method
Author(s) -
Yonglai Zhang,
Wenai Song,
Shuai Li,
Lizhen Fu,
Shixin Li
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2833442
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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 with the glow-worm swarm optimization algorithm based on the standard deviation of the features. The results showed that the proposed model achieved 82.58% accuracy by means of the 18 features among the original data set. The new map thus represents an effective detection that can help to identify patients with an increased risk of stroke events.
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