A Stroke Risk Detection: Improving Hybrid Feature Selection Method.

ABSTRACT: BACKGROUND:Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. OBJECTIVE:This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. METHODS:WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, info

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6466481 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature