Feature selection for detection of stroke risk using relief and classification method Online publication date: Mon, 02-Sep-2019
by Yonglai Zhang; Yaojian Zhou; Wenai Song
International Journal of Modelling, Identification and Control (IJMIC), Vol. 32, No. 1, 2019
Abstract: The morbidity of stroke presents an evident growing trend in the world. Stroke also features high disability rate and high recurrence rate. Therefore, the key to risk detection lies in preventing the stroke. This study mainly aims to find the way of selecting the most important influence factor in many features because of numerous risk factors of stroke. A new hybrid feature selection model is proposed based on a wrapper algorithm. The most important features are extracted from the data. Afterwards, a classification model aiming at the ischemic stroke is established with the support vector machine and GSO (glow-worm swarm optimisation) algorithm for the risk detection of diseases. The result of the classification shows that our method displayed good performance in the detection of ischemic stroke. The new method can provide the technical support for the stroke screening of mass population, and establish a referable application framework for the prevention of cardiovascular disease.
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