Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 7, 2018
Open Peer Review Period: Oct 13, 2018 - Dec 1, 2018
Date Accepted: Jan 26, 2019
(closed for review but you can still tweet)
Improving Stroke Risk Detection Using a Hybrid Feature Selection Method
ABSTRACT
Stroke is one of the most common diseases that cause mortality. However, detecting the risk of stroke for individuals is challenging because of a large number of risk factors for stroke. To address the limitations of existing research on stroke risk detection, such as not including newly discovered risk factors, we propose a new feature selection method, namely Weighting- and Ranking-based Hybrid Feature Selection (WRHFS), to select important and novel risk factors for detecting ischemic stroke. 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 including standard deviation, Pearson correlation coefficient, Fisher score, information gain, RELIEF, and Chi-squared, and used sensitivity, specificity and accuracy to evaluate the proposed method. The results of evaluation show that the proposed method selects 9 important features of the 28 features to build a detection approach. Then, we present a chart for detecting the risk of having ischemic strokes.
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