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A machine learning approach to select features important to stroke prognosis

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收录情况： ◇ SCIE ◇ EI

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机构： [a]Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China [b]Departments of Neurology, Guangdong Province Traditional Chinese Medical Hospital, Guangzhou 510120, China

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关键词： Feature Selection Ischemic stroke IST Machine learning

摘要：

Ischemic stroke is a common neurological disorder, and is still the principal cause of serious long-term disability in the world. Selection of features related to stroke prognosis is highly valuable for effective intervention and treatment. In this study, an integrated machine learning approach was used to select the features as prognosis factors of stroke on The International Stroke Trial (IST) dataset. We considered the common problems of feature selection and prediction in medical datasets. Firstly, the importance of features was ranked by the Shapiro-Wilk algorithm and the Pearson correlations between features were analyzed. Then, we used Recursive Feature Elimination with Cross-Validation (RFECV), which incorporated linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier, and Multinomial-Naïve-Bayes-Classifier as estimator respectively, to select robust features. Furthermore, the importance of selected features was determined by Random-Forest-Classifier and Shapiro-Wilk algorithm. Finally, twenty-three selected features were used by SVC, MLP, Random-Forest, and AdaBoost-Classifier to predict the RVISINF (Infarct visible on CT) of acute stroke on IST dataset. It was suggested that the selected features could be used to infer the long-term prognosis of acute stroke at a high accuracy, and it also could be used to extract factors related to RVISINF, which is associated with large artery occlusion (LAO) in ischemic stroke patient. © 2020 Elsevier Ltd

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出版当年[2019]版：

大类 | 4 区生物

小类 | 4 区生物学 4 区计算机：跨学科应用

最新[2025]版：

大类 | 4 区生物学

小类 | 4 区生物学 4 区计算机：跨学科应用

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出版当年[2018]版：

Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Q3 BIOLOGY

最新[2023]版：

Q2 BIOLOGY Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

影响因子： 2.6 最新[2023版] 2.6 最新五年平均 1.581 出版当年[2018版] 1.549 出版当年五年平均 1.412 出版前一年[2017版] 1.85 出版后一年[2019版]

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第一作者机构： [a]Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China

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